{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、服务器日志数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### log.txt文件记录了某个项目中某个api的调用情况，采样时间为每分钟一次，包括调用次数、响应时间邓信息，大约18万条数据，请进行探索性数据分析。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 请将数据导入pandas中，加上列名，如下图所示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入相关Python库\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdate\n",
    "plt.rc('font',**{'family':'Microsoft YaHei, SimHei'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_table('log.txt', sep='\\t', names=['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1  162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2  162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3  162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4  162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2017-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2017-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2017-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2017-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      "id              179496 non-null int64\n",
      "api             179496 non-null object\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "interval        179496 non-null int64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#查看DataFrame各列的数据类型等信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 检测是否有重复值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论：没有重复值，包括所有字段一起分析，以及id和created_at字段单独分析。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         False\n",
       "1         False\n",
       "2         False\n",
       "3         False\n",
       "4         False\n",
       "5         False\n",
       "6         False\n",
       "7         False\n",
       "8         False\n",
       "9         False\n",
       "10        False\n",
       "11        False\n",
       "12        False\n",
       "13        False\n",
       "14        False\n",
       "15        False\n",
       "16        False\n",
       "17        False\n",
       "18        False\n",
       "19        False\n",
       "20        False\n",
       "21        False\n",
       "22        False\n",
       "23        False\n",
       "24        False\n",
       "25        False\n",
       "26        False\n",
       "27        False\n",
       "28        False\n",
       "29        False\n",
       "          ...  \n",
       "179466    False\n",
       "179467    False\n",
       "179468    False\n",
       "179469    False\n",
       "179470    False\n",
       "179471    False\n",
       "179472    False\n",
       "179473    False\n",
       "179474    False\n",
       "179475    False\n",
       "179476    False\n",
       "179477    False\n",
       "179478    False\n",
       "179479    False\n",
       "179480    False\n",
       "179481    False\n",
       "179482    False\n",
       "179483    False\n",
       "179484    False\n",
       "179485    False\n",
       "179486    False\n",
       "179487    False\n",
       "179488    False\n",
       "179489    False\n",
       "179490    False\n",
       "179491    False\n",
       "179492    False\n",
       "179493    False\n",
       "179494    False\n",
       "179495    False\n",
       "Length: 179496, dtype: bool"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检测是否有重复的记录\n",
    "df.duplicated()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检测id是否有重复值\n",
    "df.duplicated(subset=['id']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检测created_at是否有重复值\n",
    "df.duplicated(subset=['created_at']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 检测是否有异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论：没有异常值。所有字段没有空值；res_time_sum、res_time_min、res_time_max、res_time_avg有差异较大的值，但均属于业务正常范围。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id              0\n",
       "api             0\n",
       "count           0\n",
       "res_time_sum    0\n",
       "res_time_min    0\n",
       "res_time_max    0\n",
       "res_time_avg    0\n",
       "interval        0\n",
       "created_at      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检查哪列数据有缺失值\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.866490e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177370</td>\n",
       "      <td>108.419620</td>\n",
       "      <td>359.880351</td>\n",
       "      <td>187.812208</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.686579e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.485881</td>\n",
       "      <td>79.640559</td>\n",
       "      <td>638.919769</td>\n",
       "      <td>224.464813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.625420e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825183e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811432e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981397e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.343909e+07</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.866490e+06       7.175909    1393.177370     108.419620   \n",
       "std    3.686579e+06       4.325160    1499.485881      79.640559   \n",
       "min    1.625420e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825183e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811432e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981397e+06      10.000000    1834.117500     116.990000   \n",
       "max    1.343909e+07      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  interval  \n",
       "count  179496.000000  179496.000000  179496.0  \n",
       "mean      359.880351     187.812208      60.0  \n",
       "std       638.919769     224.464813       0.0  \n",
       "min        36.550000      36.000000      60.0  \n",
       "25%       198.280000     144.000000      60.0  \n",
       "50%       256.090000     167.000000      60.0  \n",
       "75%       374.410000     202.000000      60.0  \n",
       "max    142468.270000   71325.000000      60.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看各列数值型字段的数据分布\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2018-03-15 16:53:59\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看created_at字段的数据分布\n",
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用箱线图查看count字段的数据分布\n",
    "df[['count']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用柱状图查看count字段的数据分布\n",
    "df['count'].hist(bins=30)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看res_time_sum字段的数据分布\n",
    "df[['res_time_sum']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看res_time_min字段的数据分布\n",
    "df[['res_time_min']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看res_time_max字段的数据分布\n",
    "df[['res_time_max']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#查看res_time_avg字段的数据分布\n",
    "df[['res_time_avg']].boxplot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 分析api和interval这两列的数据是否对分析有用，如果无用，说明为什么后将这两列丢弃。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论：api和interval这两列的数据对分析无用且占用一定的内存，可以将这两列丢弃。因所有179496行记录api均是统一值；所有179496行记录interval也均是统一值60（min和max都是60，std为0）；"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看api字段的数据特征\n",
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#查看interval字段的数据特征\n",
    "df['interval'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  162542      8       1057.31         88.75        177.72         132.0   \n",
       "1  162644      5        749.12        103.79        240.38         149.0   \n",
       "2  162742      5        845.84        136.31        225.73         169.0   \n",
       "3  162808      9       1305.52         90.12        196.61         145.0   \n",
       "4  162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2017-11-01 00:00:07  \n",
       "1  2017-11-01 00:01:07  \n",
       "2  2017-11-01 00:02:07  \n",
       "3  2017-11-01 00:03:07  \n",
       "4  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除api和interval两列，存到df2中\n",
    "df2 = df.drop(['api','interval'], axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>179491</th>\n",
       "      <td>13438800</td>\n",
       "      <td>11</td>\n",
       "      <td>2783.48</td>\n",
       "      <td>99.24</td>\n",
       "      <td>489.90</td>\n",
       "      <td>253.0</td>\n",
       "      <td>2018-05-30 23:06:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179492</th>\n",
       "      <td>13438866</td>\n",
       "      <td>10</td>\n",
       "      <td>1951.10</td>\n",
       "      <td>85.37</td>\n",
       "      <td>529.51</td>\n",
       "      <td>195.0</td>\n",
       "      <td>2018-05-30 23:07:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179493</th>\n",
       "      <td>13438917</td>\n",
       "      <td>3</td>\n",
       "      <td>494.17</td>\n",
       "      <td>103.95</td>\n",
       "      <td>211.47</td>\n",
       "      <td>164.0</td>\n",
       "      <td>2018-05-30 23:08:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179494</th>\n",
       "      <td>13438981</td>\n",
       "      <td>9</td>\n",
       "      <td>1798.28</td>\n",
       "      <td>101.11</td>\n",
       "      <td>433.30</td>\n",
       "      <td>199.0</td>\n",
       "      <td>2018-05-30 23:09:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179495</th>\n",
       "      <td>13439086</td>\n",
       "      <td>6</td>\n",
       "      <td>1017.97</td>\n",
       "      <td>74.45</td>\n",
       "      <td>298.97</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-05-30 23:10:21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "179491  13438800     11       2783.48         99.24        489.90   \n",
       "179492  13438866     10       1951.10         85.37        529.51   \n",
       "179493  13438917      3        494.17        103.95        211.47   \n",
       "179494  13438981      9       1798.28        101.11        433.30   \n",
       "179495  13439086      6       1017.97         74.45        298.97   \n",
       "\n",
       "        res_time_avg           created_at  \n",
       "179491         253.0  2018-05-30 23:06:21  \n",
       "179492         195.0  2018-05-30 23:07:21  \n",
       "179493         164.0  2018-05-30 23:08:21  \n",
       "179494         199.0  2018-05-30 23:09:21  \n",
       "179495         169.0  2018-05-30 23:10:21  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 7 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "created_at      179496 non-null object\n",
      "dtypes: float64(4), int64(2), object(1)\n",
      "memory usage: 9.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 使用created_at这一列的数据作为时间索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2017-11-01 00:00:07\n",
       "1   2017-11-01 00:01:07\n",
       "2   2017-11-01 00:02:07\n",
       "3   2017-11-01 00:03:07\n",
       "4   2017-11-01 00:04:07\n",
       "Name: created_at, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#created_at字段转换为datetime类型\n",
    "ind = pd.to_datetime(df2.created_at)\n",
    "ind.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>2017-11-01 00:00:07</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
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       "      <td>88.75</td>\n",
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       "      <td>2017-11-01 00:00:07</td>\n",
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       "      <th>2017-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
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       "      <td>149.0</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
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       "      <th>2017-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:07  162542      8       1057.31         88.75        177.72   \n",
       "2017-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2017-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2017-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2017-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg           created_at  \n",
       "created_at                                              \n",
       "2017-11-01 00:00:07         132.0  2017-11-01 00:00:07  \n",
       "2017-11-01 00:01:07         149.0  2017-11-01 00:01:07  \n",
       "2017-11-01 00:02:07         169.0  2017-11-01 00:02:07  \n",
       "2017-11-01 00:03:07         145.0  2017-11-01 00:03:07  \n",
       "2017-11-01 00:04:07         189.0  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#created_at列设置为索引\n",
    "df2.index = ind\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:07</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:07  162542      8       1057.31         88.75        177.72   \n",
       "2017-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2017-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2017-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2017-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg  \n",
       "created_at                         \n",
       "2017-11-01 00:00:07         132.0  \n",
       "2017-11-01 00:01:07         149.0  \n",
       "2017-11-01 00:02:07         169.0  \n",
       "2017-11-01 00:03:07         145.0  \n",
       "2017-11-01 00:04:07         189.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#删除created_at一列\n",
    "df2 = df2.drop('created_at', axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 179496 entries, 2017-11-01 00:00:07 to 2018-05-30 23:10:21\n",
      "Data columns (total 6 columns):\n",
      "id              179496 non-null int64\n",
      "count           179496 non-null int64\n",
      "res_time_sum    179496 non-null float64\n",
      "res_time_min    179496 non-null float64\n",
      "res_time_max    179496 non-null float64\n",
      "res_time_avg    179496 non-null float64\n",
      "dtypes: float64(4), int64(2)\n",
      "memory usage: 9.6 MB\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 分析api调用次数情况，例如，在一天中，哪些时间是访问高峰，哪些时间端访问比较少。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论：下午14~16点，晚上19~22点这两个时间段是访问高峰，凌晨2点~上午11点访问量比较少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用折线图展示一天内api调用次数趋势\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%H'))    #设置时间标签显示格式\n",
    "plt.title('2018-05-10 API调用次数')\n",
    "plt.plot(df2['2018-05-10'].index, df2['2018-05-10']['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-10 00:00:00</th>\n",
       "      <td>1.200839e+07</td>\n",
       "      <td>3.824561</td>\n",
       "      <td>823.072105</td>\n",
       "      <td>121.697544</td>\n",
       "      <td>360.102105</td>\n",
       "      <td>207.842105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 01:00:00</th>\n",
       "      <td>1.201137e+07</td>\n",
       "      <td>1.774194</td>\n",
       "      <td>289.923226</td>\n",
       "      <td>139.279355</td>\n",
       "      <td>188.542903</td>\n",
       "      <td>163.193548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 02:00:00</th>\n",
       "      <td>1.201340e+07</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>169.613000</td>\n",
       "      <td>156.338000</td>\n",
       "      <td>165.134000</td>\n",
       "      <td>160.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 03:00:00</th>\n",
       "      <td>1.201428e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>237.920000</td>\n",
       "      <td>237.920000</td>\n",
       "      <td>237.920000</td>\n",
       "      <td>237.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 04:00:00</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               id     count  res_time_sum  res_time_min  \\\n",
       "created_at                                                                \n",
       "2018-05-10 00:00:00  1.200839e+07  3.824561    823.072105    121.697544   \n",
       "2018-05-10 01:00:00  1.201137e+07  1.774194    289.923226    139.279355   \n",
       "2018-05-10 02:00:00  1.201340e+07  1.100000    169.613000    156.338000   \n",
       "2018-05-10 03:00:00  1.201428e+07  1.000000    237.920000    237.920000   \n",
       "2018-05-10 04:00:00           NaN       NaN           NaN           NaN   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-05-10 00:00:00    360.102105    207.842105  \n",
       "2018-05-10 01:00:00    188.542903    163.193548  \n",
       "2018-05-10 02:00:00    165.134000    160.300000  \n",
       "2018-05-10 03:00:00    237.920000    237.000000  \n",
       "2018-05-10 04:00:00           NaN           NaN  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2['2018-05-10'].resample('1H').mean()    #每小时取平均值\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用折线图展示一天内api调用次数趋势\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%H'))    #设置时间标签显示格式\n",
    "plt.title('2018-05-10 API调用次数')\n",
    "plt.plot(df3.index, df3['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 分析一天中api响应时间，可以看到在业务高峰时间段，最大响应时间和平均响应时间都有所上升。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论：在下午14~16点，晚上19~22点这两个业务高峰时间段，最大响应时间和平均响应时间都有所上升，最大响应时间上升明显，平均响应时间略有上升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vRosWLdCjRw+cddZZGDVqFEaMGIGpU6f6u4mEEEIOHSi8AocWLx+0b98e55xzDj7++GN89dVX6NOnD84++2zU1NRgx44dOPvsszFq1CisWrUKrVu39tx+p06d0K9fPxQWFuLEE0/EVVddBQA4/fTTUVpaioqKCsydOxfDhw9Hr169MGPGDGzZssW0rWOPPRYvvvgibrrpJmzZsgVbt27FhRdeiEWLFqGyshJvv/02RowYgYMHD2L16tUYMWIEAKS88TYhhJBsRosbpPAKmqy1eLmxTIVFYWEhACASiWDMmDG46667Eo6///77eP3113HppZdi/PjxGDZsmKf28/PzY89zc3PRtGlTAEBeXh7q6+sRjUZRWFiI1atXO7Z10UUX4brrrsMjjzyCuro6SCnRtGlTDBkyBLNnz8Z7772HWbNm4eDBg2jSpEnsvLq6Ok9jJoQ0IAyuJ2FBi1fg0OKVAoMGDcL06dNx4MABSCkxZ84cAMqeiSNGjMCoUaMwb94803ObNWuGXbt2+QrQb9WqFTp27IgZM2YAADZu3IiSkhLTuitWrMDw4cNRWFiYMJbhw4dj8uTJaNu2LY444gi0b98ehYWFmDVrFgDg3//+t+dxEUIIOURgjFdoUHilwNVXX41BgwahV69e6NGjBxYuXAgAGDZsGLp374533nkHo0ePNj138ODBWLVqFfr37++r7+nTp2PcuHHo2rWrrVvwwQcfxEknnYRrr70WJ598cqz8ggsuwIIFC2KuRQCYNm0a7r//fvTo0QORSAS5ubm+xkYIIeQQgcIrcEQqKRHCZMCAAbKoqCihbNWqVejdu3cDjahxUVJSgnPPPRcbN260rMP3g5AGpmg2MPZC4NQLgHGzG3o05FDil/lApA54axdwWLv09TtEtbTNzkxtYocQYrGUcoBTPVq80kD37t0T/j3//PMZ2c8333yDSCSCaDSKxx57DL/85S9DGSchhJAsIUONM9lM1gbXZxPr16/Pin4++OADXHvttcjPz8c555yDZ599NqCREUIIySoY4xUarixeQoh8IcSPQoiX1Nd3CyG2CCHWCCEu0tUbJ4QoFUIsF0L0V8vyhBDThBBbhRALhBDHhXMpJFXGjx+Pbdu2YfPmzXjllVd8pcIghBByCEHhFThuXY1jARQDgBCiG4DfAugD4EoAk4UQTYQQvwAwCEAXAPcB0FKj/xpAAYDOatmEVAacqTFpjQ2+D4QQQoh3HIWXEKI3gNMAvKEWXQngDSllhZTyRyiCrD+AqwBMk1JGpJSfAegghDhSLX9JKjP1awDOt+nrNiFEkRCiqKysLOl4QUEBysvLOek3MFJKlJeXo6CgoKGHQgghJFQ43waNbYyXEEIAeAbAnVCsWQBwNIAVumqlADqq5e/pyrfqyjcDgJTyoBDioBCijZRyj7E/KeUkAJMAZVWj8Xjnzp1RWloKM1FG0ktBQQE6d+7c0MMghBASCozxCgun4Po7AHwlpVwvhNCEVz6AqK5OFEC9j3LPNGnSBMcdxxAxQgghJC1QeAWOk/D6HwAthRDDALQF0AKKBayTrk5nACUAthnKj4JiDdPKNwghmgHIk1Im7xxNCCGEkMxAcK/GsLCN8ZJSniWlPFFKeQqAPwF4F8AHAK4TQjRX47/aAlgG4EMANwohcoUQFwBYK6XcrZbfrDZ5A4CZIV0LIYQ0MjgpkpChxStwPOfxklIuFkK8CmAlgGoAt0oppRDiXQDnAtgIoBzAr9RTngMwVQhRoh4bHsjICSGEEBIOzOMVGq6Fl5RyGoBp6vPHATxuOB4FMFr9py+vBnB9iuMkhBBCSLqh8AocbhlECCGEEAsovIKGwosQQrIW4VyFEF/Q1RgWFF6EEJK1cFIkIUPhFTgUXoQQQsihyI7NQPFKf+cyuD40PK9qJIQQQkgW8D9dlMfZqYgnCq+gocWLEEIIIYnQ4hUaFF6EEEIIMYfCK3AovAghhBBC0gSFFyGEEEIsoMUraCi8CCEk2xHM50UChjFeoUHhRQghhBBzKLwCh8KLEEIIIQZo8QoLCi9CCMl2ODmS0OBnK2govAghhBCSCGO8QoPCixBCsh0G15OwoPAKHAovQgghhCRCi1doUHgRQgghxAIKr6Ch8CKEEEJIIrR4hQaFFyGEEELMofAKHAovQgjJVjgpktDQFmzwMxY0FF6EEEIIMYfiPnAovAghJFthGgkSFozxCg0KL0IIIYQkQsEVGhRehBBCCDGHAixwKLwIIYR4Z9UC4C9XAvX1DT0SEgaCwfVhQeFFCCHEO48MA+bPBMq3ht9X0Wzg/efD74ckQ4tX4OQ19AAIIYT4pCEnxXQG9o+9UHm87Dfp67Oxw+D60KDFixBCCCHmUHgFDoUXIYQQ/3BiPkRhjFdYUHgRQgjxDl1RjQO+v4FD4UUIIdlKQyZQZfLWQxsK69Cg8CKEkGyFkyIJnTR+xhrJ55nCixBCCCGJ0OIVGhRehBBCfMCJuVGQzve3kXyWKLwIIYR4h5nND21o8QoNCi9CCCGEWEDhFTQUXoQQku1whSEJnAaweDUS6xqFFyGEEO/QFUWILyi8CCGEeIfCq3GQ1ve3cXyWKLwIIYQQYg6FdeBQeBFCCCEkEa5aDQ1H4SWEyBFCfCaEWCuEWCOEuFAtv1sIsUUtu0hXf5wQolQIsVwI0V8tyxNCTBNCbBVCLBBCHBfeJRFCSCOjQawSnJgbBQyuD5w8F3UkgF9LKbcJIYYCeEwIsR7AbwH0AXA0gM+FEMcCOBvAIABdAAwGMBnAKQB+DaAAQGcAtwKYAOCKYC+FEEJI2mCM16EN39/QcLR4SYVt6stjAXwP4EoAb0gpK6SUPwIoBtAfwFUApkkpI1LKzwB0EEIcqZa/JKWUAF4DcH7wl0IIIY0UppMgYUHhFTiuYryEEL8XQpQDuBfAX6FYuTbrqpQC6GhSvtVYLqU8COCgEKKNST+3CSGKhBBFZWVlPi6HEEJIWuHEfIjSAK7kRvJZciW8pJRPSinbARgL4FMA+QCiuipRAPU+yo39TJJSDpBSDujQoYOX6yCEEJJO6IpqHPD9DRxPqxqllO8AKASwDUAn3aHOAEpMyo+CYg2LlQshmgHIk1Lu9z9sQgghDYsmvKL21Uh2w+D6wHGzqrGrGqcFIcRAANUAPgRwnRCiuRCiN4C2AJap5TcKIXKFEBcAWCul3K2W36w2eQOAmcFfCiGEkLSw5HOgdI3yvJFMlo0OppMIDTerGlsD+EQIkQtgJ4DhUsrFQohXAayEIsRulVJKIcS7AM4FsBFAOYBfqW08B2CqEKJEPTY84OsghJDGR0OJnj9c0PBjIOmB72/gOAovKeUSAD1Myh8H8LihLApgtPpPX14N4PqURkoIISQDkcAjw4Bv3gJmc5I+ZGiQGL7G8flh5npCCCH+iUYV0UUOTWjxChwKL0IIyVoyYVLMhDE0Ur74P2CIAGqqgm+bMV6hQeFFCCHEP7SINBxT/6g87t4WL/vXvcAH/26Y8aRKI/ksuQmuJ4QQkolkwkSVCWNorJjtWPDuROXxktuD6YPvb+DQ4kUIIcQ/zOPljh/mAOXbnOt5IR2iiHm8AocWL0IIyVYyYaLKhDFkAw+cC7Q5ApixPYTGw9irkzsThAUtXoQQQvzDidk9e3aE1HCY7wHf36Ch8CKEkGwlE0RPJoyhsWIW4xV023Q1Bg6FFyGEEP8wxuvQppGIoXRC4UUIIcQ/nJgPPfbtArZvUl8wc33QUHgRQkjWkgkTVSaMIQspKwW2FwfTVtDi9zenhtc24apGQgghKcCJ2R8jjlYeU9nfMqwYr7KS+HO+v4FDixchhGQrmTApZsIYMoFPpgDvTDQ/ls33iMH1gUOLFyGEEP8wuF7hHyOVx6vuST4WzeZ71DjEUDqhxYsQQrKVdFoIVi0EKvc37BiylpDvUZjvAd/fwKHwIoSQrCfEfE4AUH0QuPtM4OErk49xYnYmLItXmHm8NOhqDBwKL0IIyVbSNVHV1ymPa4sabgzZTFju2ENtr8ZGAoUXIYQQ/zDGy5mwY7zSYflKC41D5FF4EUJI1pIBExUtIs6EfY9CbZ/vb9BQeBFCCPEPhZczYVkFD7UYr0YChRchhBwKrP4O+HZWA3TMidmRbHbHMrg+cJjHixBCshX9RDX6DOUxlUzoThzcD2xdn1iW1Tmq0kRWC4psHntmQosXIYQQ99x8vKGAE7MjoYvTEN+D0rVAyZrw2teT1QLVPRRehBCSrQQxUe0vB/63L7D5R3/9NJLJMiVCczW6jPFK5T36zxPAyF7+zydJUHgRQkhjZsnnwOaVwMv/z7oOhVdqhG3xyoT34K1/AAs+aOhRZAWM8SKEkKwlgAm3fSflsazUphs74ZXGGK9tm4A13wE/H56+PgOhgdNJSBn+CshJ9yuPqcQYZoKATAMUXoQQ0pjJy1cey0psKmWIxWv0GcC+suwTXo3B4kVcQ1cjIYRkK0FMuFob1ZX++knnpL+vTHmsr09fn0EQeh4vFxYvkjFQeBFCSKNGnZTtXFGZIrw0tL0jG5pv3gaGCOCHOfb1wrpHWruO7WeL8MqWcaYGhRchhBD/4iqdMV456pRVV5u+Pu145Brl8YFz7etlcwJVEjgUXoQQkq0E6Wq0rxTuGNySq4YlZ4rFyy0NHeOVLa7GbBlnilB4EUJIY8bNZGdbJ42TZY4qvDLF4uWWsGO8DhXh1Uig8CKEkGwlkAk1ReFFi5czod8jCqtsgsKLEEIaM6m6GtO5V2MuLV7m7R8iFq9sGWeKUHgRQkjWkqYYr0xxNWarxSsVcbp7O/Dt+/Z1DplVjY0DJlAlhBAC3wH0DeFqzDqLVwr36HeDgZLVwEe1QF6TxGNu83iRjIIWL0IIyVbStaox04RXtlm8UnE1/rTeRft0NWYTFF6EEJLtpLQPXxamk8g2i1cqrkar+1sfASr22NdxaiOdlG8D/jMuM8bSwFB4EUJItpPKZJayxasBgusbk8Ur1obhPfj7yPgWStkgZp64HpgyBtj4vU2lLLiOAKDwIoSQbCVQV2MIWwYt+gQo/8nXsEzJVotXGC7hL17VHwy//1Q5WKE8Zts+myFA4UUIIQT+rQ025/3xIuCuM3y2a0JOrvLYGC1eSfdZJ5S5qjGroPAihJBsJRuC63eVehuPHbnqqr5ss3h5ifEq/wl48ffJ52SC1SpsGsM1woXwEkIUCCEmCSHWCCE2CyHuVcvvFkJsUcsv0tUfJ4QoFUIsF0L0V8vyhBDThBBbhRALhBDHhXdJhBDSyAg6uP6F+4Abu9nX0WiIBKrZZvHyYnH6283Am08By+cYmkhB/DYSQZMtuMnj1QLApwBuB9AOwEohxBIAvwXQB8DRAD4XQhwL4GwAgwB0ATAYwGQApwD4NYACAJ0B3ApgAoArgrwQQghpfIRk8XpngnOdIMfglXSKvSDwMt6Ias0z3vOGTukhZYoC32UfjQBHi5eUslxK+bZU2AWgBMA5AN6QUlZIKX8EUAygP4CrAEyTUkaklJ8B6CCEOFItf0lKKQG8BuD8kK6HEEKIF8JyNYYyicoQ2w4RLzFeltdmKE8QQQ73ZddW9/1bEa0HNiwDhghg6Rc+Gsiy9yxEPMV4CSH6QrFctQewWXeoFEBHKNYvfflWY7mU8iCAg0KINibt3yaEKBJCFJWVlXkZGiGEND6CFCB+rVpW54VqlUrjJP7dR8C2Tam14edeGK1LbsSvVZ1RA9z3O3uaeXl9PbDsS+X5glnu2yNJuBZeQoj2AF4BcDOAfAD6T1IUQL2P8gSklJOklAOklAM6dOjgdmiEEEL8EmQer4R6IYqjdFq8HroYuLVXio14GK92bQ+eZ15ud45VnepK9/3/7Wbz8vpI/L325XJ0c07jsIq5El6qdWoWgLFSykUAtgHopKvSGYoL0lh+FBRrWKxcCNEMQJ6Ucn/KoyeEkMZMIAIkQFej/nkYFi8ngREWqa6i9HIvtGtLOseFqzEI4dL6cPPyaH18TDkhJESoq/UmELMYN6saDwPwPoDHpJQfq8UfArhOCNFcCNEbQFsAy9TyG4UQuUKICwCslVLuVss1GX0DgJkBXwchhBA/pGJJAZQJ2Wu7qXIox3hZtpEGkpQFAAAgAElEQVTi++QWLVeakWi9zuIVgvC663Tg5h7Bt5uBuLl7owGcCmCiEGK9EGI9gD0AXgWwEsA7AP5XDZx/Vy3bCGA8gFFqG88BKBBClEARXg8FehWEEEL8oU3WZu6jmNXFpfBKsH6FaPHKZJeUmfjxJIiMdS1ElTBJoOrUz+Yfgefvtq9nZc2K1sfP82Xxchib7VZChxaO6SSklI8CeNTk0OPqP33dKBShNtpQXg3gev/DJIQQkkzIrsb6OiA318HipRdYFm7HoGgoV6MXolHlnhnLUiUIi9cfLwJ2bgGuuR84/BiLShaxWNH6uMhOxeIVdkqKLICZ6wkhhJhP2pE662Malq7GEFc1ZrLwMhOyYaSTMD2mq7NhmXXbdvfPShjpY7xSEV5a3/X1QE2V/3ayGAovQgjJVsLeMsir8LIKtA+cDBZeZtatUCxeDq7GO/slt6GJKj/Cq74esfvuy9VoaHfCrcClzX20k/1QeBFCSGPGbhKObc2TITFeXhKoVu4Hdmx2rhc0ptftI52E23IvdTRLlS/hFbG2ePkR2Vb5whoBFF6EEJKthG3xqo+4qGPhamzodBJ3nQ78T5fgx+CE2djCsHj5Ca6PWbzsxmPjapQW6SRcfQ49flYPYTckhRchhDQmolHgudFAyRq1IEVXo1UC1VDTSbgQMqVrnOuEgZnICsT65yaXmlvhlWKMl1GchfFeTxkTfJsZAoUXIYRkK34mvNI1wHvPAg9f6dyWV1djwqrGLAyu/+DfwC0hZKkP2zLp1poUE1V+00lYWbw8vNdut0Lavd19m1mGYzoJQgghhxCxOB0X1o+UVjVmYQLVZ+5IvY2ULV4+YrxcuxpdxHi5cjUa0mWk8n5IaW5lO4TTTtDiRQghjQlj9vHQVjVmocUrEEKK8bLbMshrjJfdeFylk0jB1Wism9HvZThQeBFCSNbiY9LyYvHyGlwfdoxXujLXp5Ko1cniVVvjrm+r1zFMhJfjfXHharRLJ2G5ZZCb+2RlwbI4lxYvQgghvlmzSFlhV7kvmPaGCOCZO32erG0RlGN4rZvoNFeSmxgvK8tWQ6eTSAVNPPnpx2nLoL073be1+juXItDrqkY74WUhC7770HqTbFcWPQtxaHkuhRchhDQOtq4Pvs2X/6TklFo5P7g2P3ghNYtMjsHVqG9L2/ImUxOohi686v33YyY49WVOwkvf5+gzzMutznEar/E9N8PK0jT9z/HzUsnj5dai58fitbcMmPpH6xQnGQKFFyGEaCz6BLj5eODL1xt6JOERm+jsJjaDZcR1cL2uXph5vMJ2NcauKSCLl/5e+LV6BiG84CKPl53g0e6LJuAO7FXEjtl92r0deOJXSiJbs7HGC7yPw4pn7gBefxxY+rn3c9MIhRchhGhs+kF5XLckpA4sJpmt64Ci2T6a8yNADK5FszaMiTb9JFD1KlqKPgX273ZXN+ssXh4WHbjdqzFBmASYx8sOY4zXsA7AtYebtzfxNuUHjFEEhWnxqq5UHsMQ/QHCdBKEEBI2TpPIzT2Ux9lpWOGVFKeTYmJOK1ejl8mvch8wdijQdxDwj2+cx5N1MV66e2GVfsNPu8ZjYcZ46c/TPjuxxRcm7/XG75XHVh2MjSS+DFIkGceXoWT26Agh5FDAbqJbbiM0/LTrNPnGJsmALF5BxHhpK/1KVrurn20WL7248Cs0AtmrUUsnYSH+DuwF9pXZtO9hr0ZNlO0rA6oOxMuTrt/Fqk23WO0lmWFk9ugIIaQhSNdS9mgUuP+c1Nsxy+lkhdEqYCuqXAivBKHhwaUWjQKzX1YD+N3EnenHkWUxXgnbKoUgvLRjs6c6NOIQ4zX8CGDPDuvTveTx0sr+ejVw24n6A87nmvXhBmkxvgyDwosQQsJGPxFs2xiPZUo55YKJi8mtxcuN1cKN0LGK8XIaxxevAX+7CXjjyXiZ44SZLlejek1+rFOmebz0LlgnV6OPGC+t/ZcetG/aKYFqXa3D0CyEuNP7saPYum6gwsti1WWGkdmjI4SQdJKOLNo3dgNG9gqxP5cWL9t4nyBcjQ6ipUIVn8b0Ck+MUPKU2ZHJrkbTvRp192LnFmB/uY9m3QTXO2B0IXslZgF16y40IUl4WYyFFi9CCCEpo006WhxNqCkXHI672TDZazoJL8H1+nHo6375f/bnOY0nCGLjsejnkynAtk0O51qUPXcXcP1R1n1bXZsbV6Mde3bELUF+A/w/nWLen5fPsZ/M/F7bZnA9IYQQc1KMIUoluN64V6OZlcCrxSvxZIdx6OK6tDacLBV+Yrx8JZm1sXhFo8A/RgL3DPTQn6HMyaVn3jDw+hPAllUu+zTwyRR3ezW6GopHV2Ni5RTOdWrasHAkQ6HwIoQQI2G5KoztBhVD5CW43s1ejUkTtIsAfGNbnqwgHu9DKpnS3WArvNRjVkHoTqsanbAab201MHUsMPpM9+foESJ1V2OsvwBdjUEmULXa0ijDYB4vQgjRCNuFlZKlIHaSw/kBBte7CWa3ylzv5drqPVq80iW8zO6lWxeqHreZ1L/7GFi32GJMar8HtUzwHmO8cnLi73mqW+rYfY6/naWMx+q9NN4/q/vp68ePjfU2g6DwIoQQI0F/cVu158fykKo4dJNOwmjx8uNqdJuhXehcja5dRF6EVxRArvv6QFyY2Fm8bPvTEakD5rxpU18nUp6/y7qeXb+uLF45wVm8jGJJ396fL1Me21nFsYUY45VK4ts0ktn2OEIIOZRJOTO6jxivJJETUnC947XphJfX1WhBuO5sz7GZwN3kJ9OjJRK1YuMPyirOjT84tGsQXl7cy1p9o6DesAz46EXnc5NIwXIbpqvRTUxiBkDhRQghDYUvi5fJOd99pDseQIyXxl+v0hq1ac9nOgmNmoPW7jUrGjTGy+m61HMWf6ZYzpz6/+Yt5XHeuz4XMej6tEUgKU3Inf2UPRUBoKrSRRvaWIwWr1TSSYQQ45VyfrxwofAihBCNVH4pr1oArFkUfn+prmrUJnA3mevdtGklRNxe2wcvAE/8yl1dX6sa/Sxg0ISXWaC8g6tx93bFgjVmCPD64+5TN7i2VPo4F1De7xybdBJXtnJuI96h9/6trKde3p/57ylZ8J2S9mb4JtkUXoQQkoSPX9t3DwTuOt2+TiqTjlUbyRXsD8dSNxg2yY5GgMeuAzZ8725LGGN7xnpuY7z0hJG53o+4tYvxcrqu956NP/9pvYuYMJfuWaPY8ONqhMHVqMdLbq+UPsceLF5b1wObVsTLnroRKF6hW2BgbJoWr+xj/VLlHyGkkZOmGBGrSWfhh4rVxCxlgdsEqVZY7bdXVwt8PQMY9yskCc8wVjWmYl10Ord8W2r9pOJqrKmKP09YOOCEy9WovgkwnYQfV6NVDjG74PqbjwduP9HkkMNiFVq8sojfnKr8I4SQIHG7tF7j3aeVxw3fJx9L2HA5BVejMYGqX6yEhZ/Jb9dWd/WcxqzPDJ9SHJ2PVY21VYmvHVM3uLz/Ka9qNAmu900qaVFSiPFyK+Zp8SKEkGwjpDxAbld0aROHWSLIwFyNLoLrAXWS9uFq1J8z/z2TE1OJb2vA4HqnSb22Ov7cjcXLr6sxsRH7PgA1j5cmvFLM45Uk3Dy4jd26Ke3czk4/ZGjxIoSQbCHNy9CtJggrdyAAR3ee6y2DLITX5h8TY2jqI+4FgVXm+pLV9mOyY9XC5LJULCx2aPfENo+Xk/Dy6WqUEq4FrtZ2wrlOmKxq9EsqiYBTWtXoFONHixchhGQn6doyyMrtYpVdXl/HCqfj9RbB9Zb1HYSX5SSXqkBUj999ZnKZl4nei/UjySLkw9Woj/GCB4vXa4/Y10tqJ4A8Xm5p2sxQ4CO43nJVY+Lre48Dpndw2ZYRWrwIIYQkYJwwrAKN7facSzgngHQSTkTrzfsxtmccT6qT3+7twFczDIU+XY17dgBvT3AhUAzCy1dw/UFDfQ8xXn7zeHkVXkah5HR+rmGTm1TyeCW9d4mvJx4F3NgDMHX3O8VwZcmqRm4ZRAghoWORJd7y17/26OBqNMXt6jiXMV5OFi836ST8WLz+38XJq8z9WLwggUeGASu+AU67CDiml3VVN1sleYnxWrsIWFvkMLwAhJcbISp0ezUa2/IqvFIJrv/z5cD7umStfvZqtOqPebwIISTLCHurEcfAYhcWr3QnUI06ZF+3ivHyktPLjB3FyWV+XY271fQSZvezfBuwc4vhnBQSqOpdjZuWA48Nt6/v9lrCXNXo9P7kOFi8Njlsd6T1r1H0qb5z53Mvbqq8R07CKkssXhRehBBixGuM16oF7uo5uWiMW56EGuPl0uL10wZ/lphoihYvO2uT12DuuhrleV5+vOzDSUD1QSX1xA3HKuXGGC8/rsZUVwxaEUQC1aBcjcb6j17r3L8VVn3PfCb+vK4WWDBLf5J9WxRehBByiDNmiLt6SROexQSoPZpavHxkhNdjlbneirvPtK9j5VLUr+7zY0m0FT0eXY2a8NLu58IPgadvByb/IbGqG+HleP9TmPR9LWIAXLsarTLXO4nJvCYexmKBVaoRPysinVYD09VICCEEgLOlwegqcbJ4mU5ahrL95cA/RylWg4S2PVj1vGaur60G/t8l1mOyLNMfNpk8U7V4aedVHVAe95rsDAA4WLw85OVyO774C+t6qaaTEMJ6r0YnIeVk8UoFX0LVhbV03kzgr1f7GlLYUHgRQkhYjOytBHZrOMXWuAkO9uqie/H3wPvPKdsBAfFJ98vXlbpuJlEviT3raoHild7GbIbZPfAT4yWjceH1z98qiwWc3Kx2ljUna0oqosSubb1Y8rOqUEprV6PTNR3Ya2zMuT+3SYi1sTv+EBDxfi3Hq3M1PnwlMPcdd2NIMxRehBCiEXRwfclq4Ju33MfWGGNUzMajT0bqJnYqUpv4Wi+UVs5LXXgZVzU++xtg1AD353vp024rH7t2NOG18ENg0SepbcbtaKHxeq26+nbWNL3Y8BNHJqOwdDXWVSe+Nrq4K3ZbjyVVgvps6NGPrz7ivf2QofAihJAkAk6gamXJsrKAWVlc6iPA73/h1JlFHybir642ub6bNhPaNwiv7z5yd77jhGtzjv7cbRuByv3JdfXnWK62dLB4pdvVaCu8dMfqI/CcQDUatXY1GvcozslNfH3l3Ymv/Yglpy2EnMSwEO6D5/XHa2vcjS+NuBZeQohmQogeYQ6GEEKyEt8pASwsXlYulaT9+jyIJrNJV0ZTt3hZpcRwOt/XqkYT4XVjN+Cu0z2MD86uRttNskN0NdoJiiThlXCiy7YtLK/GdBrG2MKWbYG+Z7sbp2X/Tvc6gLb0bWrvsdGalwE4Ci8hxGFCiJkAdgD4va78biHEFiHEGiHERbrycUKIUiHEciFEf7UsTwgxTQixVQixQAhxXBgXQwghGUlsD0DDhGm12bAxrUSsfrJFpKgQSGjVKkWF2QbJQcd4OSW29IJtcL3hWOkam3YMfQvdnoVWgiWVBKpe0fdhtxG2/n178DxgX5l5G5bn68SIk3hMsj4JoMVh3vqzw9Tq6MLK7NbiFY3GU4fUZafFKwrgWQD3aQVCiG4AfgugD4ArAUwWQjQRQvwCwCAAXdT6k9VTfg2gAEBntWxCQOMnhJDgCD2Bqsc8XsbjBuG2vOonnHYy8MdjbdrUxEUsa7k+/YNbEeFhVaMf65ZplzbWJj8pCPS4tXiZCZSw8nQ5ta0XZWsWJR5zcz9e/B0wf6Za30l4xaXBrjxgV/QgPLs2XeOyLS+rOGU0ngKjNgstXlLKA1LK/yLxR9WVAN6QUlZIKX8EUAygP4CrAEyTUkaklJ8B6CCEOFItf0lKKQG8BuD8gK+DEEKCI6xNso0WDac8Xg4Wr+2RCgDAkkKTNmLn2Fi8ogG4Gl1tPxOQq9Eu6N2yHR+uxlRWNXolwfrj0tVo14YV+j0kncSjLri+wxlAh03jDMInhOB6V39zur+Llx4EfjfYvFrp2qy3eJlxNIDNutelADqalG81lkspDwI4KIRoY2xUCHGbEKJICFFUVlZmPEwIIe7YsAz4dVegYo/HE0OyeMVEj8HVaGWdskoE6VXk1FTFM36b7dMno3B1zV5WNbo+34XVwqqdVC1emvVGswBZ9Z0OV6PbjcVTTaCqx9HVaCINvOYN8zoWr3nl3ngS+P6r5HIAePOp7LZ4WZAPxQWpEQVQ76M8ASnlJCnlACnlgA4dOvgcGiGk0fPaI8D2TcCyL9LTn1vrjZPFy8nVaBBepr3qz3l+dPwXv1l8T9AWL6uYsaBdjV7TSehxM8Hv3m49Brs4rFRxu6rRiOeVlA7Cy2zHBL0YcyU+3Y7JbT3dqkY3VsdcVXgdQhavbQA66V53BlBiUn4UFGtYrFwI0QxAnpTSZv0vIYSkgO9f5CG5GDWcLFZJebycg+sBQOib0be5dV38+SPXANP/7G9Vo22Ml4tVjVrZ7JeBT6a46A/21iazY6sWJidu1Z+jx0l8TR2rWEr0/dx/rnV7QeE2xsuI3XvYvlNymR+Ll16MuQ3md4MnV2PsJOcqh6DF60MA1wkhmgshegNoC2CZWn6jECJXCHEBgLVSyt1q+c3quTcAsLDvEkJIAPj6MgfCdzU6bdViXNVoDK43Wrxs3HFm57/6V/sM6Fa4jvFycDX+7SbgHyO99W3Wjtl47j4TuK2v9Tka+s2i7erW1SDhepbPUR4n3uZ6uK5wk1cMcIjLUs9re2TyvorND0uu7iHGK47HGC+3FjrXP5J01lQ3n50MjvHKc6oghGgJYCmAlgAKhBA/B/C/AF4FsBJANYBbpZRSCPEugHMBbARQDuBXajPPAZgqhChRjw0P+DoIIcQEnxYst4Jtbxnwl8uB6kp39Z0sXkbB5WDxMl+I7zCpBZ3Hy6+r0Vf+pgBcjYD1++tmK57tm9z3HSRustrX1wPNWiZmmvdz74OweNXXWR/7v0dNxuLwN2cVS/jW34Fj+wCnDU2srwnQmipkGo7CS0pZAaC7yaEvATxuqBsFMFr9py+vBnC9/2ESQogH/LoavZ73yWTgx2/d1zdOnlZuOn06g4MVwJ7tQKfjLYWbMClLaM+qTynN6xixTaBq4S7Vs3MLsD+AbWf0Fq9Ny4HxN7g5yaTMSnj5yHGWKm77cBP/Fa0HmuQb2jdLieEnxstjcL3dVj0bv/fWlrE9/TVNekB5nG14v7SNve0EYAPBLYOGCODJGxt6FISQxoBTxvEkV4pUtgi6Wd00xE0OqYrd8V/5QVi8ThiYPE4rrNr6egZwU7f46x+/9WnxkthQAMhoFJj6R2DjD87nGEXGto2u+7J8fwIlQOFVH4m72GLHbBLResKjqzHiUvDo7qntnUgQXi7umWa1++hFYFgHxTqdIVB4AcDn0/2fW7kPeOsf6fllRAjJLLz+3Tvt1WhctReNAmuL4sfdbBVz1+nAvT+zHp/XPF5uVz7G+rOoe2Bv/Pk9Z/ma/L9rHkH3/sALNSvcb35sHPtzd7kTsNF653QeQRCkxctMeJlavByuwyyQP0iLVwLxeEzbT0S0Pt7vV/9xblaz2lXuA/btCi83nw8at/DSf3j8JsX7x63ApPuBlfOCGRMhJDj8ftmG9SXttFejxy2DLOe/9Ust2je06Ub8uF756JEZ4z2f8l0LZbw/RMo9iCCf8WVmgjPo5KkAULLaXT27VY2ayIlGgCZNE4+ZZt+PAjtLrNszu7de00m4FV668UXt/uz07c0YZ15H/35p443UKo/Gjb8bkMYtvPSrHfwuEd60XHlsVmhfjxCSRtIU4+UlBxHgvFdjUjoJ48RvLtws5yunPFRRFwlU3dQxjCcsyvKU9g8XBe6Fl5XwcCqL1iPpesKweC39r7t6dnNUvd7iZVjVaLXn5euPWbdnKrwMFi9t7nPLhmXm5boVyM6uRg+fr9gm2eo8b7pSs2HInJE0BPrVDna/Jr6dBSz4wPzYrlLlMYPUNCGNHt/pJDRcnufZ1ehxVaNeDNxxMnDvIG/9OboaI87XYGHxmtsS+HlfoE5/q0IOSN+pxksfLpp5d2UlFJmIEWNMktl1h2HxcouTqzEaVf4deVzisfpIsuiIRoHd27z1ZRRejwe1Xi6+NtfR1WjHgxfE52Mgfs0x4ZU5c3QjF166vasuKbCu9+fLgD9dan5MW0Ye5saphJBE1i1xl0Hct/AKSTy4zeNllkB14w9JaSscNY5TKoF6F8LLIsbrtu7A162A1c0M/YUovParwqs5cq2Fl7YQQT8mI2YCyrj6TR9TpC9rKByFl3r8+P7AX3SpMusjyekhovXx7PwmrG9Sh1nGTf2MezXu3+Vu3E7o7rGjq9Hus7X088TX2jXX0dWYWQSZ36MhfwkRcohTuqkEo+69GPV19cqqtN/2B4o+sT4hUxe7uM1cb5VANQnV1WhZzcHi5UZ4yaipyOmozmfbDLHcvY6vwIUn2DfpF22k9TAfE4DEbP2AhVvRRMRoE7T+vEwSXu/90/rYzs1x40BuHtDtlPgxM4uXjAKVe2FFj741uMz4HurFWzQK7C93N26VvbnAq2Y7Aer2anS9qtENwmDxMstN1kBkzkgaAr3FK1Vo8SIkNO78x3l4rvVHeOaZvwJ7diiFB6wnjjgBuxrfeEpJQVOrfpm7FHgSEpU5SPyeGCKAhYYQBmOC0DC2p6k3CC83MV6RWnzWCijPA9DzNABx4bVdH1IkJda0iGK20VpiQ50ASvOd6yUOqR7YutZd5XWLk8sitcllZhavpHQSUeC4E931m04+fwUo+lR5npuXKFKaH5YsOrasAkqt759UP/59+ukK9RavSK1nIXTT8cD/9ABWNjMecWnxMrNA2mGM8cqlxSszoMWLkKxgf84BAEBtpFpJKAq420LFMw7naaupDuzx1OpL2IzCgcDGWsN5b//D0L0/i5f1YReuRidkFJU1FRjSF7i0N2Ium9bq7d+fm1jXK7/pChx9mqEdC7R5uX7Vt0CFy/fg6duTy6b/ObnMaPEyi/Gq9zj56+l0vL/zvJKblxiv9uR/k4WXy4D+H5vrXuiFl4+5s1RdbFllfJ918Zi2nx7PFi91vFzVmGE4WbykBD6c5K6tMDdOJaSRI1WBkSMEUKUKL5sv4m+xB83OBHbWuLGK6TvSJlWLyVXLhl25z1DfnneEEsi8ps7BPeO0SbaxulPHTsH1LmO86uqUjYZXNkdsAtPcmxG9lcKVFTKRWW2Vx0oPs1HK/oUyk1QKRivYpN8pliE9xSuAg/tNm9ydB5RZ7QVzxsXAVJcWulTJzQNaqPszXnw70LFrMCv69OKt1r/RIunTJmVsI+tAXY3a51p7XzMoj5fjlkGHNE4fnqX/Nf+1pOPT1sC+XOBauhoJCY1oTHjluBJeT+VsQHUO8M3eH3G1l46cNuHVfjXHBIbFVPHTBqCVSUCLdPie0G8VZDcOrbrZlkGJFZLLfMR4RSO61Duq+NSm4Vr9nL7Pf3ZwN9OiNlJbl5RfjMLr2/eUf3oePM/y9HZnKI/SLKVjE5vFW0GTkwe0Owp4cSVwlLrbXxDxTSlavCzjEGurgEsU/2PUTpF4nWP1f8sZZO0CGrvwqnaweLn4cA3tozxeS1cjIaERW3AuhCtXY3yButeOHFx8MYvXXqXO5h/N693U3TQWSDpZxp3yeBmI2jtnYB5crzunujI503lS/XrUFStb8wggNolpU3lCOgl9bsQA+bANsEYXG1QfhvBSrS6hYExq6pJqAezLA47wst2g9hk9Vhcdn8kWr6/fiD0N1NUY1dXPoPguoLG7Gp0+PMbNRu2gxYuQEFEtXjnuLF7x0zzG4zi5GrXklAf2Ap9OVfYgtEKXYFJoEtDkB9rnrYBphyPxuFGAWVDv9IPPyeK1rwyODstdpYgs+jj+2mA9SBBebvfnA3AwB7i1u+KiA+ytWJecANyvS08VyrdtbTXGdQKanxlMc1vzdXc235/F65cnAEee7vEk0w2uU5/qv4ro8n75sXipj0mfti9eiz314mpc3ALJKS+s6mfQikagsQsvK4vXirnKJq4fvJBYbpc3iDFehIRGVOhcjQedhZcWEya8CK/XnwD+84TagFRWfRlTE2gT6J4d5qvlnNi7I6nogr7AzbG4a28xXtGojfi0yqmlF157kseTRF1tTFzpLV71vRRFUKsXTMaVgTZxNS8dAUw+AqhTZyEvYioUi1ddDcZ0MQn+9sGiQqDzacAUTVD7tHh92drHSQYjwJBXhuDN1l5MZuYMrpwVf+HD4uX0lu3PTRTXScyelvBywClITnmhR/8jIMNcjY1beFl9eO47W9nEdf7MxHI7MzpdjYSEhiYfcnJy4sHNQbkaK/crYm7qWF0DErilZzwZZ31EcUXlq/6ubRv8BetauSb1/QKuVzVG1bgk05HU1cLUhvDdR/Hne3e6sgpqAfRCIubKiqiisE4/ixgtXk8vSM6krmLs1YuYCuXbVudqTLX9lepqwG/UGHfkF0A8LHCPnbAICp2BoLa+Fp9t/AzXHlMRbB9BWrxU/no08MrhFge9oLla9TF7FF4ZhNc8XnYxAHQ1EhIaMQuWy1WNsaBzNxavK1sBVxyWWGa0NP3pMiUAeO9O5bWd8DL9ESZ1/9tgjO1as8i2etQsH5VGbZWzqNq7w5Xw0ixee5oAb+UoGc8j6kKBBFej8T1pVmi+yACpCa+wY7zqUmzfeG1SjaN7+ih3589urXM/m7Rnx5Syb/D5RiWL+5Z9WwAATTw0cNBGFRx5GvDgccKfxctsFazLfj0RE166HwGM8cogvKp2/T5QRuhqJCQ0ElyHrvJ4aSf6zLlkPE+LcdLSEFSapxQAYCoIXbs8jbFdxgSrSV3ZCa9qF8JrJ9xM63ohMiynSOlbHWOtVYzX2P8oAd4uLYOZ4GqMPQ2ofa2ZuvwmScfWFwCrkpKJKlzYR+9+9maBG7nlNVzwygUAgEvEtmoAACAASURBVOOfVRppGXUvPM7ta31sRz7w5FHSeWGaDVbCKynGb5RNpn47tMUi+s8iY7wyCK8WrztOtj7mZt84QogvpPpzub4+4jG43uffpZNgqa+DpSPTbFyehZfh0QJbV6ObVXoHK6wn0XtfAnoMAGA+WcYsXrk5+KgN8GQnJMZ4/Xy48mghvKSh2I2YihoeA0V3v2pTnBnlSeckvK4ysbgc3x844VR37VmJlRph2CvTgmYepvqili4q+fDwaJdgKbyMBd1d3hwj2r3W/x3S1ZhBuLR4RQHsTP7BkggtXoSEhpbHqz4acRlcryB8/1k6CKVInbUlx1R4uRyIsZ6FlX12a6AkH4iunGvdloWrUQIYewywUYv31tynRobeAvRUAuiNFqDZrYEltYrLsS4vFxefADzYBearGvX3afS/LIe7rIWzu0kTZ0FZvLbmA491Vt9tH67G8jxldd3EjvGyKACprizUmqnKS22qtRIrv+0G9D7VJnGrSm4DWnzebavcIw3XFq9mhf461D7z9Qyuz0xcWryu6QUccbqyyaclDK4nJDRimyPX18UtXq6C6725GqtygPu6ABX1NgtpcvOUL3UPwkuo3w++Ms2bcGEf4JRTHARIbbVpj6uaAU8crXyvAQAqdpufL0QsNYFRiFzYB1gR2aV0o7fmGFY1XvfWdfigiW5rnwLryfRXPYEbelgeBgBUaysgAxJe1/QEHjpW3bzZxtU4rhPQ0iTNxKATldV193aNl9XkIMm1VZWb2oCtxMpXamjiXhPhNW/LPAzsPBAA8DPZ1nVfBQE7b67qrdwjzxavQg8bfuqJZazXC6/MkjqZNZp04zJA8N12yqPZh1tDek3uRgjxgPJlGonWeUon4TXG64UjgQmdgHEHFlpXKmhhatm5uTtwezckp1QAYpYst8JLAtjhYGXf3cTB5VZjbvHS3GixCbCuBmeeBDx4bLxOtQD2Vu+NCQiryRIA6vTC6714XI6UEjNWzsClhavjxx2sGF+2sj0cF1721QAoqRyu7WlfZ7d6j3/dA0CdtcVrTBfggMkP79XNk8tqhG4VrvpYlZOa8DLbx/L9tsAG1c2ovT/PHxk/PmjqIES1lac1B1z31SIkG4JQU2q4Fl4dOgO9vSVVkwC25aqfDv3f4a6tntoJm8YtvDwGCNbY3K1INPU8KYQQczQ3hNcYr3q7PFcm1Gj76tpJmqbNVeGVOINMOwKYdCTM3W2qxctxqxt1opx4lJI4c71J3k296NDas4zxMhFe2sSnrXSLLvkMC1sCT3aO1zn7RKDN+DYx4WXneqvLjX8xRnXWs+qISXoGnfAyE6F2Ag9QLJKAuy2DRh4PvNnevk7Cp8OFq9GNjK/JAaR637SVfFU58TOLmwIbPOZTPea0ZLF5Ra/4c80C+NtuhrGoltu6KvfpJJr6FF6LCoFNNunKhIOIN31Pf369pzH86RjgqBMPKPGGHpL5ppvGLbw8LomttfvyqbdZXUQISQnNghWN2rgav/wPcPfAhPr1HoPrYy5K/Qy78MPESs0K7V2NZl/4qqByHI0qlD5QvSzFTZUYpMPOiFfRiwJbl1tdtak4rTMIry0mk2UswDrH2eJVq7Pm6MdTFYl/v045Qn1SoAv2McFJJrtyNXpIVpqQg0znarQKrncT+7UrD7it4vOEsirdB+q1DkD3/onnbHWxSUrsh/8zijVWP8Rqi/HWqvOS47ivGA1A+XzmuVCXb5kI2tNPBroOiL/+6jDgIn2CU+1HjVuLF6BsaeWA/rxHj1YeLeMNM4TGLbxqDmJ6B2BJC6DSxZ2ws3jVmbkXCCGBoAkpGamMx1MaRcUT1wOrFgDRaDwmzOPqK2lmQfp/lyRWsnA1xjCxLmgxXnYCRgKK8Hr/udiv/1woMUgVujAHvSjQJh0hgTu7AmeepGtw63pTt6dm1Wuinmzn0pTqnaiz++7TWbz011dVFxdeO5oAfzsKEG+eFbNamc3vTrFb1Zrl065SQaL/TwL4KR8Y3ym5zwRBsrfMvNyqvgV/6xR/Hguu1434vwZ36odtlCz3H6pi20qE1QhgbkvgHaksuND/OKi2GFeNurl5bbsjzStonKF8xvufDGxxYY1bWei8zfP1PYFPdGFa2rZZnixeLoSXvr0c/Rucwbk1G7nwqsKNPYD+pwCFA53NyLUCljEjtVFavAgJi5glqk4Xq2LlaozUxoWXZvGa9S9giEhMsfDao0qZWT92qxALWihb7bwzIVb0sj5H6FM3mlyAavGymbjrAaB4BfDPUbFpOlf3daONSG9518rebwe80BFYqE8F8K+7TVNKHFTjhbSp08paAgCVwiRJqoE6ncVLP7aDdfFQjjoB/L2zcnCPzZytn0QXFAK/6pFo0ag2cTXuzTV4IwwWr1oBXNwb+EMXYHNTxFZqGvvDxy8mjFdD379T3B0Qd4cCwPLmymIGvavRuA3Qd6r3dZH6OPzC5M3VAeWH/9knAVe/cTWAxB8HVTkA/rMt+RzN1Xh0D+A9mzgvdQ/S710uJGxb4+zCr0r6XKkiXj9wXQyX6czqUXjlZ8kat0YuvBJjvBa0BJbaWMJrcgD88Zemx+hqJCQ8tDxeqDsQ/0K3+kWrcxnFYrzeGK88PvBzYNwNynNtX0Z9P+pjjl1evoIWSX2P1q1qw4Zlye26EF7zDgPw9O0AzK0C2oSVYPFyssAcTE70qt0/zaVkZ8nfHzloOR4NvXDTT7bfbPkm9jwiANlcWYK3Kw/YY7FCXJ/b6/LewOsdgLm6TQX0wfVb84H/7Qa0OVPZTDpGk6b4qE3iOctUQfHCkYDUCUU3lq27j4s/76a60n5/LHDGSTBFfz8XHKbk6iqq2GheGcB+nRCNApi3e7lpve8M+bX0Q6/OAdA22apVul+xjtXJCNDMMLn1HRR/nmtvwerdvnfC60IXxqQaw0pOobrmIyeeDUCxQn5cEBeDfi1ey1oA25oAzx0JVGdW1ghLGrXwkgbhddZJwKmnWNevFQCKPjE9luRqjEZNv4BjbF0PFH3qcqSENGKiUeQIRUytqt6O5gOBVzrA2uJVF/8RFNEEkpbGYPVC4IvXlOcdOsOItnJZ2AmvpslL2RzDYqLxGK+7j1PifLTXGj/XGTo0gaa35GiCa21Bcj09n7QG1mhJNU1yFWq5sjRXY42ujffbAvt0k9f+6r0A7C1eVXnxSVsvwka+PzL2vE630u/kfkDbM4EHdIJGz2+6Km5BrctzdfdFE3b1ArjnOOAlVWv8V7UiRQHcfsReXKwTYvoxje8MfJm7L/baSlDqr/efJtv8PNU5WQhpmMUCP7LuNfPKUBZSAMr9eegYy2q4XKd95KnnJ1u8AHRt0xVmaPPTi0cA4mdqaM0/4sLYSXg1a5KYpdUo1vWf43faAm+f3i8h9g9ALCYyMng40O98nHUi8MvmKyChWPsqzNRIdSXqAXzRyvpv7GcnAd36A6O6WVTIQBqv8Ko6gNrd3paYxj5sZku0jRavGeOAO/sB65fGz/nva/GtRm4+Hhg71OOgCWmEzH8P+5oof18lKAegLv+3FF41ycH1zQ2zZDQKHNU9oej9tsDf1fgcYfwhpU+DYAgQt1uhNqqrGtuki/F65qh4vqoKi1/omqDST3DahD5YJ0TMPCsX9QF66ZJ+f9sSyB+oTLijj9MJL/VrTC9MLu8N3KTbqmZ11XbUCaBu6M2W16hPDprsXlKICF2KDwf+1VFxC5oaQHTCy0x0bioAJrXcZ3qORqWoR1UOcOPxQKXF/ddErp8Np1LJev+vjs51AKDyodcTXKpVR3UBAByoNXcnavPTU+rnu8S4/iA30YfaQiS+bt4k8ceGFlO2vDkwr2XiPb66N3BNk6WxVBZGlu74HhhwITarfzc7miiB+R8bUo29t/o9zGhajlu7A+f1BebbZNSvyhJLl0bjFV5P/Ao1Hv+sYr9kTNJQ1Bq/qJfPUR53KpuUYtNyYPwNCXEEAJh4lRAnyrZguxpwHM2NuxGXRHZi8u8uxkNjb0isX1eTHFxvTNw58TagRWKU88e62JuckrWJ9Q/TLePSTShvtFNWqFVYGAye6wj8TmfZMYoFY36m2LjVR701yswl6Catwl+PjgfHP3tUXGy8305p39iuPoXFlZE5uPs4oKK1dQLOgzqXkuXqOhHffSAVYukkYAikVtluEoNlHNPbTXbjvbbA9MMTy/VWG83iZXY9TtFNdqvfbenSxzZXpJ690eqY6w4AbjqsGDWRGpQfLDetX6emO2qpXuT+Xz8EAFjZDLj3OGBj88QPYoFIHMgvuyeG2GifmZP6AYNOshbceuY0URadvLjkRRSJuAvcaleYK2ZcgesqPsU0dUVsqfvFqgC87f2Zbhqv8MovsA0q1Vips7DGvqAO7EmqV23MdK25KvapK2XWfJf4qFHlPrEdIYcqkboIRv/+KqxYpsS3PPzwb/HBO28rx6r2x6wINTrhtfRAMW4t/AiPNTW4cSK1cYvXN28Cd5ySvLrvk8nAglkJRfqVe0lzp26l3E2183Cn6tF5+XBjRXv0k7JEYnwPoE5gJ54TdzUaLF5GreFmcjGuktO7Eh89OlHcAcAKQyjQmi7HYnehdfBrlW5hkdUE/H8dgPI697mkAMTEth4tBqxeJL9HW/KBIpPgcOOKv5ebleN6k8SqetFTJwC0Ptx0C6PLeyeX6bGyojlRduo5zpVUth/YHlslqPHB2g8s06fU1dehsrYSJeo93XXSQERlFH1PVVyd3Sb3A2bHP10FOYlqqFVBK3Q+LO6ar/7FdQnH3QgvPZt69ok939rLfDGBkZJ8ABeYLFyx4I32wC97OwvlhqDRCq9P5A7LL81LegMHcpRkcH11JvvYl6aJ8DoYqQV2lgAH9ia6IsvUvdbWLIo/6o9X7vV/EaRREI1EsWnlBnz89ky8OulfqKr0ln8uk4jURFBdmbzS7qVJE/Bsi3cxZcbj2Ll9Jx6NPo8Xv/oTAGD7gR2xetW5cQFVtmNdQhuvdABOORlKcH1E+bqtB4CN3wM/fI3/tFfcbbGtvww/evRxPcnCqzDWx8vYghdUl9A+lxYKrT290Jp2eLLFqzIHQGHrmPCaqTM01Ypk1+SYLtZ9aoLKaEVZo/sxub7AflUjAESOPBZ7qq2/p/SrF63a2u1iNaAX6kXy5HXsacA9JiFOp/Rz16be/XYwB8Bh7UzTDH3ksPvOnuYudq02YfO+za7r3vHBHQl50gDgmjevMa17ypGnYN3udRjy6hCUacLr4C5MXDAxod4T38QXm+SJxA9ak5wmOLIwHrxfbXBy9zzTmznq25/iO0P8slnyYoJ8kaxetzYFMPxBANYuej1/OkZxXzb5WXzFaKbg8mvj0OPfcgM+tghk/LAt8PAxycuetx9+KrBziSKuqg4AH/47dqwqUgvccAw2NQWObX0scnaof0RlJcrjWlV4bd8E/LQh3uiBvcDhJgP5bDrQqj1wuvkqyiBY9PUCbNq4Cr1790PPk05AfnMXWfxIYMioRMm6LVi96gds2rwKP+3agLIDW1Ae2YY9KMeevH3YnV+FnfkRHNB9Fjf8fSX+/Kd/WjecoUx+bgL+XPoA+h3ohlnPJrryZq15EWgHVNcfwMx3XkEkB6iGMrGUHIxv4lyVG//9WmaYzH+txk1VV+2HjNQATXXB07l5eKyzcu6WpkBrk00r9IHWSQEABS3wZrt4HwBwTl/7CcDMsabf73XuYcCRhtDQg7nAD3IfflQNbDN0aSpqchz2izWwPV9JR2GM59G3uTvPflUjAOyv2Y+P139seVyfof7PR7sfXyqE4UYq0X39jegJDNvRNJZ6wwt7WzQDqrz/OPpo3Ueu6y7ettjyWNtmbbG7StlB4MJuF6J/x/5Ytn0Z5pfMj9VZVbYK4+aNSzhv7BdjY8+FYa/Jw1scjtYFcV98tYjiU51rvspub1MTJiyYYHv8jStewxXvJlrV/tMe6LbmP/jgBODz1hYnAmiZ3xIVtRXoVg2sVzXw6ScD+2sq0LKpTaBYGmm0wqsQebZfOPokeBpjWy3BGEBZ4nq5+gb+THmoqqvB6mbKTvHAZtzSEpi8Horwqq3Gl7t+QL8TTkPR1kU49b9vIPaj6YDJL8lVC/He5BtRKPJw3p/nAb2UvDOLvl2I0waekVzfhIo9FdhWshU9TuqVdGzef7/GU+/+Lz5qt05xr2wBmn0EdKzJRYeaFmgXaYO24gh0KDgGndp0x3FH90HvPv1wfJ+eyGvaaD8yrpFRiZ+Kt2LVih9QvGUVtpZtwM4Dm1Feuw17sQu78/Zhd/5B7MyPJMYG5QForWxS26G2CdrVtsCxNZ3Rr64D2jc9Ch1adsajdc9hy95VgY635kANmhZ6DKDwQHVlNe4ccz6mt52HaAEgkLi0fs+uPZjbSrFe1USqsHD9R0AroEYoE3pJVTxuRdsvL6euJZa0jQBInuD2Pn4V5BGKotEsR3XRCDaqsUv1QlnKvrkpMFDn/dJbvA7kKhsojygDrtwNyCO64FrDr+ZvbPYVPOo0YOSO5PJNuvipKUcAJxtWy1fmACfnfm3a5kn9gCZSwG3I94tHAM84BGt/0Qo41mHOXLbdZnW2gbnaPdl6GsYMPx9Dug3B4JcHuz7fLVEBUzdgKhgF6q2tSk0ztDuhiZ6Gol2zdthdtRu/O+t3GHv2WBTkFeDJ+U8iots+yyi6jNQbPmNHFh6JVk3jH/gaRDG0j/Gs4GjSLFkgbc8H7pr/V/z/9s47vIpi/eOfSU9IAqTSQuidRCmKCkSKiMAF0YuA8gMURLAhKIqCElC8InJFRUVFuIB4VcByVVRAOiKIdAIhIRCKIYUS0tt5f3/MCScnOYGoECCZz/Psk5PZnd35zs7uvDvvFC6xdraHiwdpuWkk128C52wfeHnX0LJ+lbYW9fmL0tOcwWfWQ4D9V3FW5nm7yfXmB9sMrxXffUvvlgXAb1AdJm2YRMMgPaR8edJhqlLEt5+fx6IP/smw5gD5PPdud16JPMjCZcsYeWos034ey4uT7ZuIi5OSmMLN/65BprOFky3zcbLOLP3L2o28vnwkK/wPofyh1/kbuLVub06ciSUxI54USyIpzmfY6XmSJPd4CtQ2yAPi9Fbla6iZ7UJAbhX88/3wd6pBkGcotf0a0yC0Ja1at6Ve0wY4uVRMD7ZYhMTjpziwfy9xR6P4I/kwieePciY3gTMkc845ldNumSS759m7oJwAX70GWmCOK355XoTk1CY8N4AA91oE+4YSEtyIhg1a0LxlK2oE1rDrOFuU9ya/R0r+qb+sITc7l5X/+5aNO74kKn0r0V7HiPPKo26WM3Wz/AiR+jSoFk5Y085EdOtJQM2/UPMUYev6zTz+9V1s90/jjrONqOFUi8VVN3A4OpaGTfWowvc+fOWCCy7HkkUc0fq3yiX9fDoDc9dcOF+h4eWScgPrahYZDl/EfX8u4zQHrC1GhYbXww1tE4emOcM9zeCoB+RttrkBi7qVCkc3Lg+AxxJgad5XZdacDyS42ZYvKUrxinxsMdfY0Nsbw3l7F2pR8lTZO6hPL0PrU74TfHSJSc2LExEawfr49fi6+/JI20eY+cvMkgf9+CbTP7gNpaB7g+6sjtNL6GwYvoHO/ym9L9OAFgNYGrX0kmm41BqMf4XiSyct8ki5/Be5DNxS5xa2nNjicJ+zcubhNg/z7Opnmd51Oq7W0YoRoRH8fOTnMl/jrNhb48HewXaG1/uHvyzTeZ655Rne2PJGma9biJP663VIYdxkSxYd63Zk07FNAHYtdlebSmt4qUxnKKMrvkrsA2Qc7Ah9xuDbAWJ/T6ChkxMFRUY3ZWek2M3DAzC0MXx44hj7Y/UXo2eOP1nup/lPEOR4eJIiWXwX9xPNt7Xiwy+m8N6M71g8Yxgjqp0g4ERrLGcaMSPsK3ZOa0e7ar3BCX5O/pwXubjh9cT0nsT568b4datX4+nuxYxlI1nhH60NrtRwIofN54Yb25R6jvz8fA7FRrNv704OH9vPidMxJGUdJ0VOkeJ8liNex0hyO4KoLZANROvNNx+Cc1wJzPHGv8APf+eaBHmGUiewCY3qtaZVWFvqNAxBFZ/j5SqTkpBC1J7dxB2J4kRSrM2gkmTOOp/jjFsGKW55nC3eV8VXz4cUlOuCf64XdXJq0DovgAC3WgT5hhIS1IgG9ZvRolUYtYJrlWpQlZXAHHfOKscjlxxxNPoo33+/hB1HVxKt9hHlc0Zr8IRqLorm6cG0ya9PouUPjnkksslrmza4D3+EcyzUz3ShbrY/dWhAo+o3Et48gs5de1At6NIvsbTUNPr/1JnzPhZedH2cabPf4b13X2dxygZWrf6Khk0nALAq8VOCqziR5mIhWzKJ8tX6cpzy+N/3/7M7Z2HH5dzvZ8DIWy+ES5H5quLdteEDuhVriw8sDLad44kG2ugC3f8DoFkmVCllgPG7NYFcW8t0oFcAyZmlV8obHLSElWVJMoDfL2J0XW0+u/czpqybwls936LDxx3Y9OAmvon+xvHBmQFYLODszIVpBaZETKFlUOnNJCqnCq2DWpfJ8LoSzCg5rVupBHgFkHKRMhAZEUnk+siLnsPd2Z161eoRfTq67BcGvhr4FTVmlbSWb693OzPvmEm7Wu145tZn7N41H/7jQ8Lnhpc63URxgryDST8bR92qdQmqEkRtn9p0Du3M/F3z/1Rap3ebTm5BLm9ve5u26dCx21je2vrWJeOJCEPDh7Jo96I/dT2AjDzdjHz8/HHCgm2z3P4dY+5yU2kNr9PnpITh5e3m7bBgum+cQUZ8LcL7L2d33mq2e4NzmsVuAdCMjNNQrIvU4iBomZlJcvIe8IYqc9egqh/n5NA+ILqi+DHmAD9GPcIn1XcyZN7LjM/6L/UyqzHups20D/fh9YXdWR70M54pmyEA1vudYt/OvbS6sfSRICedjuBRoGfxHb/yHqK8M8AfeqW25qX/m0+btu1KjVuIi4sLLZq1pEWz0l+UuXm5RB3Yz/79OzlyPIqTZ2NJyjpBComkuJ4jxvsIyW6HgU2QDuzTW7U8CM5xIyDXh4ACf/xdahJcpT51ApvQuEEYrW9oS426f/IzvBTOJJ0has8eDsdFcTIplsTUo6TknOSsJHPO+RynXTNIds8taVD5aIMqMNcF/1xPauYG0zI3AH+3mgT7hFA7sBEN6jeneYvW1K1T928bVGXFL8+HM66OR4fl5+Sz7qeVrN26jKjUX4j2PEp0lRw95YA/NMrw5Nb0loQHdKJH5wF0vC0CZ2f7TiyZmZls3LyO33at4VDiDo7mx3DYM4l1nluwqC0Q/R6uB6BBpish2YGEODWkkV8bbmzVhU5du+Nd3Tr67XQC69f/QoK7hUlqNNNeeAeAnj3uxWnJc2yO+ZrRTOBITBxbqp2i77k2rPXeSZzzYVJd9Dp0qa5ZHInZ5DgjTtwCGyahOv4LcbLQdlJ1sM5wvq2IS3ByaMmoexwM0DtYck7UUmni3/Sihle3ViXD1lw7H9t8M+gb+n3Wzy6sfa32zOk1h5vn2boy7H90PzGnY7j787sB6NesHwNbDQQga5J+fzXya0THuh3Zk7iHsT+OtZ0wy59x4+DNN7kwGq55QHP8PP1YM3QNXRd1RaEYf8t4Zm2ZBYDkVMXdpaTLe+SNIxndbjTtPrr0e6s09ozeQ9hcXQlXca1yoXK+GEFVgkjKSHK4b3LHSJ5a+TgAu0fvJnxuOAv6LeDBb/R8Zy/cNsXO8Ppq4FeEBYfR8G3bLJ9nnzvL8fPHaTqnqV0rVpd6XXi5y8u4OruyOm41k9ZM4vmbX+Zfz9bnH/dm4CXBRI05xP6U3QxYOgCAY08dI6SqrYmz+PuoQfUGnBx/kqqv2b4KQnxDOH7+uEN9L0e8xsHEw0zs8uSFObyGhg8lLbodrx67i4TMkvFcnVzt3HkhviG4Obvh4qTNDLegetzvN5ueA++h9xddsIiFyZ0m88rGV+zO8+ytz3JryK30aNgDZ+XMgl0LLuyb2/t9Rn8/xmGavQsgfcNURk48y+ytunFix5aqcAVaR/8uSkpZe/Bq065dO9m+ffsVO/+AJxqyLCAO5zw3vAqckCXfMfWprjztYPpcr7dSyTzri5vfAXKfbOHgbOCX53jkjk8+9D3jwRcB2eS9nAfiAgPvgebaddHhjxpk+Kaw1zufV09W5YXaqQx2fo9PJ+vClZyUTPO3gjnrKhfm7Ol4JpCVr8bjWcVxk127pz0oKKjKH14pnHW10OtcayYP+Zh27dv/hZz6e2RlZbF33x4OHNzFkRNRJJyLIynnOCkkk+JyjiT3bM44WGDLL08RnO2Of543AZYAAlxqU8O7PnWCm9KkURiNmzTnxLF4YuP2czwhhsTUo5zOOckZS5JuoXLNIMUtl9NuJcu3iwUCc53xz/XCr8AXP/HHz60mQT51qR3YkAahzWnesjWhdUOvqa8kgD5PNuU371gSXy3gVHwC3/7vU7Yf/oFoyx72+6SQYtXrna9okR5AM5cwOjS+i359B1OrhoMpuMtIWnoa6zesZvuedcQk7yS+4DDxHsmc8LS9aN0t0DDDnRZZXsyKP8u3DTvwuN+vfBO2lL79bSOuOjwVyEnPsxyZls3zkQ/yhtsnLG32CWN3DeMPD91SWzPHiQT3YuUisRUE74PzteDfevLjiD53sb6d49UkyoKnkw9ZliKG7M/Todsk+4P2DuLBvs1YcCSSloEtGXHjCMavHP+Xr9k45mZiGm8tueOXp+HGj8FTt649dMNDjlsY4rpCgzUlw0vhH43uRsSJ7w5/SURoBGuHrWXa+mmk5abx/ZJg7r2pCxOGtKNqVVBTbRW2TNFlacmeJTiJBz7H7+XOO8G1yHtuxw6oUQPmvFdA6wFf8sFv81l/8keYmg/izJYt0PLGNGZtmcX4W8bj666t41fm7SLhQD2eeiGZt7k1KwAAFwxJREFUJnOsIxaSm5E1eyfjfhzH3N/nXrjGwoZ5fPOVC1+Glfy4CcxV5GQFcL5qMk7na2Hx/QP3bG9yPOw/oJOeSSLoDd1B/Jx1hOZn937GoOWDuLNuf0L8/Zm3cx4A/m41OX08gPfDdjMmUT//8/vO56H/PXThfJPr/8ArR+4CIO/FPFycXBARWr7Xksdvehy/w48yONqW3u0Pb6dtrbZ2+Zv/Yj7OTrYPnwkrJ1C3al2euPmJC2FRUfDp5k3c1+FWwsOcaNwYYmJgyBBYvBjmbp+Lj5sPD4Q9UOr9P34cAgPBw8N2f6dETGFQq0G4OLnw3KrnGHnjI/T6750MbD4Ez/h+nFp7Lz/+oLBYICNDTzrv5QVOTkD1OKZ+9QlT1k2hX9N+nEjM5PfnlrJ2jTMDNtend+PePHfbc7g6u9LIrxGDlw/ms32f6cRECvffDwsW5TJj0wyevvVpqrxq/yVUWO5Az8O3P3k/4XPDAdgxagdtPmxDcJVgXvI7yGPHbR2+nCwKyzQLVI2HcfV04GdfwaD+OIkbBZF/bgDAX0Ep9buIXPILodK2eOW56hf8DRtG8vvGdwF4+mkgsuSxmam6YORmlF5xOTK6WucEEeWWxJKgbOplw1FxoV072Lt2NuBLaI0vSa5+isNW+2lOQCpKoH2dHhfOERgUyMD8vrznppv0n8wdyNt+n9N/Ygu+Hb0E19MnITFeT9SapP9m+OZQNbc6S7v/F28fX9q0/+tfin8XT09Pbmp/Mze1L31QQHpGOrt27CA6ZjdH/zhIwvk4knNOkqKSSHI7z37305x1PagPPm3ditdbVcDZy2pQ5XgSmOdH09wA/F1rEORTl1r+DahXtyktWoRRv36DEi091wv+zjVIdjtE2NNeHPDOIt8JqA6hmW60y2hCK7db6H7rALp16YGLy+V7vH28fejTqz99evW3Cz+Xeo6161ayY996Yk7v5JjEscIvmW2+0C31V5wEIrrcYRenZ8AgphbMYcFHb7M281sa5Ltxzz8H8+yOB4ECghwZXQDv7wVEN4dZsXjYz1Lut+Z5znS1X4OxYRYXnjE73jxCFsr2kgY4UqQj+IzTkF0NxIl2A6JZQCRhMZ/ywzetYfMDkOMDk7305FKl9b36ZAUMvBdcba7QmCP/1K3tdXQh3tYvg5senQu/joXUELjrKTrX6cr80Y9T9466zHy2JY989wgjgz6COlt447UXIOQXeKCP3aW6hNzJ2uN6GbKxN9tcOmrVLL5dHELjQR/TPnMkixYppgybQmoqzLoTpi+C6Y9DWho8EDiLJclPA/Duu9CtG9zf+gHmzYP7R8Hrr8ME7SFm8WIYOrTw6s58c9MA3o/4By1uOgWin6/0dPB09uHFTpGkp8MdfSAnBzZu1GuzDexXDc+Po8ga0QLWvMLBfR78q+P7zN32MTjnwdLPGbbfWo4TZ8Adz8HWJ+Bm3YLqYlEkr34T7h2CJakV+P5BTloIeNgPQPH38mdUm1GMbjeas9ln2XR0C1HL7oMfkvlp72AiJ1YH5sH5Wpy2GvVj4EJ9EFrN2nSaVgM8z/DKqFtgghu45FqNLsjOVvy3YxRJSXAiC/jPWhiuy1NhS96w8GEs3L0QIoUvW0Bqqs7j+vVhZg9bX7n0dPjoIxg/HqAjXa3ds1KsDa2ffKLzf3S70YDu4rh/P2Rnw9Sp8M47UK8ebNsGN98ML70EY8aAm7MbzQOaE3l75IVrNd29nF6DYNm+L9m+rCuvRdpaxc6cgTp19Hl/KBzYerYBL3Z+CZ89z9E6wJ0V2+D3HNi6EY4+kUj0IQvNA/U9KyiAcW0nsSJmBWNC32YGsGuXTseLES8CsKDfAlausrA88TVm32P76FmzBubOdebVV8NYOmAZH65azbx3q4Ir9GzYi8fuqQYP9ITG+sPLctT67KaGwr+PsXFvPJ0iO8K/Urmz57XVtQURuSa3tm3bypVk5MQOQiQSFv666GJr3Yb0EMa0Ep9OLwqR6O3CfostLBIhaK9Q5ZRdmGunqRd++/ceZ388Ig8/LNK1qz7fLQ+F2u+PRGo/cJusW2ef1pSkFAl63km8JyMiIiOe7iFEIj6TkYhxyPiHkU+He0j0mMZSMLmXVH3eU1o8cd8Vzb/y5vSZ07Jm9SqZO3eWvDB1lIya0FsmvDRcZr81Tb7+8gs5cCBK8vLyrnYyrzjvzHlVqk9ykjbj/OT+8Z3lzXdelrijcVc7WXbcPvh3cXpJCZFI/WddS+xPSUoRn8nIzWOrC5HIg+O6iohI02c8hEjk9ifrSNex9eyfjdZLLjyH//iH7Xl17TvswjE//7RVcMq1xen6guAfLS4eSRfCfCe6l3yuVb5w91Dhhvn6/7DFQsgm+/dCaZtzjqAKhGZfCc7Z4jJokBCJVB3TR6gar4/pPVpfb8AA6TT4F308Itz2mgyeM0NmzRK7d8zNndLtruHra/u9apXtd6ueW4VbX7fpcc4W6q2VeoP/LeOftsjAVxdK29vOOUz3sWMiX35pH/bkk9bfo8OLvfdE+va1/b7vPpGQEBFvb/v4w4aJREbah/3zn2XIQ0dbg5XCgH/qd66D/ZGvJwiRiNcjLXV+hi0S/GJ0uiMi9d/Hmkvbe9fK/K8PSa9eIgMGiGzdqrcGDRxcs+4GweekXVjttrukatufZMkSEeqt0WWlcH/1w0K9NbJunci8efbnmj5d/61x+9fiHukrazZmyKZNIrv3FMjzk3MEREJD9TG1aomkpoo8+KBIfLzIqFFlyyN/f5E9e0T++1/H++++W2suGrbwk1wJC8+XN94QOXhQ5PHHbftiY0vev6Llrei2fr31+XMVGT9e/w4IsJWTnj1Fvv3Wdvzzz9vHnz1bJDpaZO9ekRdesN/XvLnI4sX2YQMH2n57tViry3rh8+d2XsLvXmN73qzbs8/afoeFieTnX/l3H7Bd5NL2TaV1Ne7elcao0a+wbetrOFwVzCUDJls7i0QKvXrBihXg+6wP573S6XyoNRs+3QMIRNrcUas65XNHN2eovQ2SW8ALelisRwFkvyzMng1nz+qvki6DbmBts90ANEv1Ito3k047trN6eVu75nyAmW9MYu/JjSx6cwOWAguRr01l6YHtxFc5Tk5wDBZnPfTe192X89lptEx9hn1vvn65s81guCSrVsGYz5txOCSaiDO1WPdWyTVRez/ZlBX+eqj3hs5r6NSlC7WedybBw8JjWf2ZOWUJJ04cp8mn1inGI23vqaVLYcAA6z/t34PejwEgU4SwMNjr8jF0fx7eioPcwmdYP+PdExqwumYct/3Wm83ff/eX9Pn46NYhh7ifh5ZfwM4HKWz1wSkPAqIhyUHnr8tB6HpqBrmT8FuHy3M+p3xQBVBw5aYYuSyEL4TDPSBdz5lRty4cS42H83VseV9OhIRol56h7FStqlv8LheurpDnYMaIOnUgNxc2boQmTUruv5yU1dV4bXVgKUfCwn3YtnUGjpdihWaNqlA/05WOh3Qn9vBwWLIEwvdY3Sb57tSvD7m5iv4ZEwFoeqoF3bs665t/8ibI9Sbgl1EArO7xGwsXwvDhcN990KoVtAiag1daAGwbw91VBzP0fG+HRhfAhGems+hNvf6jk7MT0yZNZdfH3/NG+B6C5p+H93fRPGYe3YMewOWPTjTCLMBtuDp07w4tcwYDUEscTCUO3BOmOyaHnfemUxftInAW/TrqfEN/PD09ady4CeMK/o82Pz1DixYwbBh8/TXcdRfcfbf+EOK3MfB2DDM8tWH20kvAzhEwM4lfN3gzcaJ2j7H4J5i3hUe7f0/EwZv55YdlVCvS4b2/vQeV2g7m8SvE5l6zp1kzIMcXdoy8UPEvWgRYXMtkdN1++yUPcUx8hJ3R5VnG0drOzjBpEjz2GDxYdA1si8ufNrqGDwdvb+jQAeLiYOFCqGmdQ6xvX90PbNgw6NIFel1kTugtW3T5mTgRFiyA6qXM2VS7NrB7GHF7bBOV3X032s1UzOgKCIDRo0ue44MP4Isv/pTMErxmnQ7rShtddevqvC0Ljz5q/3+/fo6PuxgWC0yebB82YgQUbQsp7d4UpXhvh0Dr5L316/81oyvkIlOkfPqptQ9aMYYNg0OHrrzR9acoS7PY1diutKtRRCQpSWToUFtz5NGjuul282bbMRaLyNdf66ZgEZH4ownS84kmsmFtrKSl6bCtv+ULdTfIjz8VXIh3/LjIY4/p5tzirsNCsrNFN6WrAscHlJHMTJF//1skMNCm5bnn/tYpDYa/xamEVOn+RAP5evkyh/vzcvOk2xOhMvONyRfCftm8UcZPGSr5DnwCFovj68THi9xzj0h6ui1swgT9DCQn28I6dtRhZ8+KFBRoN9vBgyK//GJ73rdvFxk+XG+HD4vMmiVy7pz+2727SHi4dqelpYns3y+ycaO9O2TlSpG33xYZN07kkUdEllmlh4fr/bffLvLpp/r8f/whMnmyyMcfi9xxh0ifPvrYQpdMbKzI1Kn6eiDy668iZ85oF09Cgn5vjR2r3TUJCfqY4GCRnByREye0i+iHH/T77MUX9fugWzeRRx8VGT1aZOJEkYwM+7xs106kRw+RTZu0Oy4xUWTLFpGXXhLp0EHn3RNP6GtNmCAyYoTIG2+IzJ3r+N6cOyfyzTf63hW/f4cO2fLt2DHH8QuJjdWacnO1ayopSeffiRN6f+F5DhwQeeopfd3C7hwg8ttv+jiLRWTNGn2PVq60L0NpaSKtWml334cf6jz44ANdJ3h46PPcf7/tnA0b6rxNTRVp316kSxeby+2LL0RmzBBp3Fhk5Ei97803Rfz8tPvtoYf0/Xv4YZGdO3X90KqVvtehoSKDBonk5ek6JCvLlk6LRWTSJJENG3SePfmkyJAhIsuX67LbtKkuq9nZWlOhK3P9ep0nO3aIvPKKLjNLloicPKnzbNQo/axs3iyya5feCgkJsWnetEmHDRmi/4+K0vk5YoR+/tat0+l2cpILrtRDh/T1x4+3lQGLRR/vyIVZo4Z2CWZk6HOJ6DL9+ec6XyMibM/DihU63Rs22OeTiMjPP4v8/rvIO+/YvxuuNFxrrkal1H3ADPRqD6+KyEUnBLnSrsbLTV4eDluqLsWaNeDnBzfc8PfTkJ4Oc+boTrEzZ8KgQZeOYzBUNETg1ClbiwvA+fP6a71r18t7rTlzdEtEy5altzSdOKE7Qk+cqEeHXYyCAu3GLGyNK3w9XyreuXO6lcLvEusIXgyRS1/ncvLRR/Ddd/BNKVOBlZVWrbQX4aWXbGGpqTB9OkybpkfzlYXS9B88CLGx0LSprdUkI0OP8itOYiIEB5cMvxrMng3jxunyd7EW3IuRmqrL5MGDcKt12rz8fEhKglqljDU7Z53yrtolplB5+GFo3x4OHND5tmTJpctfVBR8/jlERpZvWS0rZXU1lovhpZTyAaKADmjDaxfQWkSSS4tzvRleBoPBYKjYJCdr1+W1WOkXR0QbTpcygAyXj2utj9edwHoROSkip4A1QLdyurbBYDAYDH+bwMDrw+gCnU5jdF2blJfhFQLEF/n/BFBi+Val1Cil1Hal1Pbk5FIbwwwGg8FgMBiuS8rL8HLDfk1pC9rlaIeIfCgi7USkXWDh8AeDwWAwGAyGCkJ5GV4JQNHufXUAM+uJwWAwGAyGSkV5GV4/AXcqpYKUUjWAW4GV5XRtg8FgMBgMhmuCclmrUUQSlVKTgC3WoKdF5NLLwxsMBoPBYDBUIMptkWwR+Q/wn/K6nsFgMBgMBsO1RqVdMshgMBgMBoOhvDGGl8FgMBgMBkM5YQwvg8FgMBgMhnLCGF4Gg8FgMBgM5US5LZL9Z1FKJWM/230AkHKVknO1qczaoXLrr8zaoXLrr8zaoXLrr8za4frVHyoil5z9/Zo1vIqjlNpelsUnKyKVWTtUbv2VWTtUbv2VWTtUbv2VWTtUfP3G1WgwGAwGg8FQThjDy2AwGAwGg6GcuJ4Mrw+vdgKuIpVZO1Ru/ZVZO1Ru/ZVZO1Ru/ZVZO1Rw/ddNHy+DwWAwGAyG653rqcXLYDAYDAaD4brGGF4Gg6FcUUp5KqWaFAurqpSqf7XSVJ440l9ZqMzaDYZCrorhpZTyUEp9qJSKVkrFK6XGKaUClFKblFIxSqndSqk2DuJFKKUOKaWOKKUmFQm/zxoWq5R6qHzV/DWKp1kp5a+U+tyq/7BSapCDOBVCv6P0KqXyrP/HKqU+cxCnQmiHUvXPtN73GKXUvQ7iXPf6lVK+SqmvgUTgWWtYPaXUSiABeKCUeNe9dnCsv8g+P6VUolJqsoN4173+Uu79O0We+VilVIFSqkWxeNe9dnBc51nDxyqljlnD73IQ77rXb+p7B4hIuW+AP3AvoNATpSUCoUA16/7RwLJicRQQA4QBVYBDwA2AD3AcqA3UAE4BgVdD15/Q7yjNnYDbrfsbAecA14qmv7T0AkcvEqdCaL+I/ghgC+AKNAUSK6J+wBvoBowE5lnDagK3AFOByRX83pfQX2TfAuCH4nlQUfRfTLt1f2Nge0XUbtXiqM6LsGryAVoAf1Ax3/mVur53tLlwFRCR08By678pSqnjgK+IxCulnIEQYDeAUuox4Az6JiSKyB5r+DKgJxALrBeRk9bwNegHvESryTXEnZRMc20R+QxARGKVUnmAp1JqFBVLvyPt3RwdWInu/S3o1ucCwAP9Yqpw+kUkHfhZKTW8SFgCkKCUurPosRVNOzjWD6CU6gbkA1uLhFUo/aVpL8LDwHyoeNqh1DqvM/CFiKQBUUqpo0BbpVRbKpB+U9+X5KoYXkVRSrVCVzb7lFJvAQ8CUehMRkTetR7XH/slhE6gWwdyHITXvPIp/1uEcJE0W5ucd4jIeaCi6S9Nu79S6jBwDJggItsr0b3PAb4HfkW3eg2GClv2y0Rl0a6U8gReBvoATxaGVxb9AEopV+A+dItGhddepM4LAPYV2XUCqFmR9VfS+r4EV7VzvVIqAFgMPCiasUA14FNgabHD3QBLkf8t6BaC0sKvZUpNs1KqETATeKSMca43/Q7TKyI+ItIQeA/4qixxLhJ+LeMozb5ot8No4AtgbBniXK/6/ywVXXsk8K6InCllf0XXD9AX2Cwi54qFVzjtRes8Lq2jQumvxPV9Ca6a4aWUqg58C7wgIr8VhouIBXgf6FAsSgLar1tIHbSvt7TwaxmHaVZKhQLLgKEicrQscS4Sfq1y0fSKyFK0i7VaGeJcb9rBcZqHAF+JyA4R+RfQQSnV/BJxrlf9f5aKrv1+YIJSahfa8H5MKVV0kEFF1w/azfixg/AKpd1BnXcpHRVGfyWv70tyNTqWob/wNwJ9ioS1Aqpaf98HrCsWxw04iW5urIJungwFgq3hQejOdnFAlauh60/od5Tm2sDvQPtS4lQI/aWkt2hHy7uAQxVR+0X0zwT+g+5QWhNIQvf5q3D6rXqGU7JzeSSOO9dXKO2l6S8tDyqa/uLarVpisU7mXVG147jOa2vV5AU0t/5WRfZXCP2laK809b3DPLlKN2IykGF94Aq3x4Aj1t+rgIbWYx8HBlt/90SPbjgKjClyvuHAYevW/2pnahnzwC7NwDwgtVieuFVE/Q60t7be+8PAJiC8kt376sAKdN+FA8CIiqgfPSIpFj14oLCsj7H+PQOctv5uXNG0X0R/lyL7I7EaXhVNf2nagWnAi8WOrVDarel1VOc1AF5Av/sOALdVRP2laK9U9X3xzSwZZDAYDAaDwVBOmJnrDQaDwWAwGMoJY3gZDAaDwWAwlBPG8DIYDAaDwWAoJ4zhZTAYDAaDwVBOGMPLYDAYDAaDoZwwhpfBYDAYDAZDOWEML4PBYDAYDIZywhheBoPBYDAYDOWEMbwMBoPBYDAYyon/B06ySaCitvkNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用折线图展示一天内api响应时间趋势\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%H:%M'))    #设置时间标签显示格式\n",
    "plt.plot(df2['2018-05-10'].index, df2['2018-05-10']['res_time_min'], color='b')\n",
    "plt.plot(df2['2018-05-10'].index, df2['2018-05-10']['res_time_max'], color='orangered')\n",
    "plt.plot(df2['2018-05-10'].index, df2['2018-05-10']['res_time_avg'], color='g')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-10 00:00:00</th>\n",
       "      <td>1.200681e+07</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1127.927000</td>\n",
       "      <td>123.163000</td>\n",
       "      <td>430.941000</td>\n",
       "      <td>232.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 00:10:00</th>\n",
       "      <td>1.200753e+07</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1121.823000</td>\n",
       "      <td>105.833000</td>\n",
       "      <td>470.831000</td>\n",
       "      <td>213.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 00:20:00</th>\n",
       "      <td>1.200820e+07</td>\n",
       "      <td>4.5</td>\n",
       "      <td>905.376000</td>\n",
       "      <td>97.177000</td>\n",
       "      <td>352.390000</td>\n",
       "      <td>185.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 00:30:00</th>\n",
       "      <td>1.200883e+07</td>\n",
       "      <td>3.0</td>\n",
       "      <td>714.885000</td>\n",
       "      <td>151.778000</td>\n",
       "      <td>382.929000</td>\n",
       "      <td>252.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-10 00:40:00</th>\n",
       "      <td>1.200943e+07</td>\n",
       "      <td>3.0</td>\n",
       "      <td>447.091111</td>\n",
       "      <td>96.337778</td>\n",
       "      <td>185.563333</td>\n",
       "      <td>140.555556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                             \n",
       "2018-05-10 00:00:00  1.200681e+07    4.9   1127.927000    123.163000   \n",
       "2018-05-10 00:10:00  1.200753e+07    5.0   1121.823000    105.833000   \n",
       "2018-05-10 00:20:00  1.200820e+07    4.5    905.376000     97.177000   \n",
       "2018-05-10 00:30:00  1.200883e+07    3.0    714.885000    151.778000   \n",
       "2018-05-10 00:40:00  1.200943e+07    3.0    447.091111     96.337778   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-05-10 00:00:00    430.941000    232.600000  \n",
       "2018-05-10 00:10:00    470.831000    213.300000  \n",
       "2018-05-10 00:20:00    352.390000    185.400000  \n",
       "2018-05-10 00:30:00    382.929000    252.900000  \n",
       "2018-05-10 00:40:00    185.563333    140.555556  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df2['2018-05-10'].resample('10T').mean()    #每十分钟取平均值\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用折线图展示一天内api响应时间趋势\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%H:%M'))    #设置时间标签显示格式\n",
    "plt.plot(df3.index, df3['res_time_min'], color='b')\n",
    "plt.plot(df3.index, df3['res_time_max'], color='orangered')\n",
    "plt.plot(df3.index, df3['res_time_avg'], color='g')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. 分析连续几天数据，可以发现，每天的业务高峰时段都比较相似。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析2018-05-01至2018-05-11连续十一天数据，每天的波形都很相似，每天的业务高峰时段都很相似，呈现周期性波动趋势。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:00:48</th>\n",
       "      <td>11406128</td>\n",
       "      <td>6</td>\n",
       "      <td>2105.08</td>\n",
       "      <td>125.74</td>\n",
       "      <td>992.46</td>\n",
       "      <td>350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:01:48</th>\n",
       "      <td>11406236</td>\n",
       "      <td>7</td>\n",
       "      <td>2579.11</td>\n",
       "      <td>76.55</td>\n",
       "      <td>987.47</td>\n",
       "      <td>368.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:02:48</th>\n",
       "      <td>11406347</td>\n",
       "      <td>7</td>\n",
       "      <td>1277.79</td>\n",
       "      <td>109.65</td>\n",
       "      <td>236.73</td>\n",
       "      <td>182.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:03:48</th>\n",
       "      <td>11406446</td>\n",
       "      <td>7</td>\n",
       "      <td>2137.20</td>\n",
       "      <td>131.55</td>\n",
       "      <td>920.52</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:04:48</th>\n",
       "      <td>11406488</td>\n",
       "      <td>13</td>\n",
       "      <td>2948.70</td>\n",
       "      <td>86.42</td>\n",
       "      <td>491.31</td>\n",
       "      <td>226.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                         \n",
       "2018-05-01 00:00:48  11406128      6       2105.08        125.74   \n",
       "2018-05-01 00:01:48  11406236      7       2579.11         76.55   \n",
       "2018-05-01 00:02:48  11406347      7       1277.79        109.65   \n",
       "2018-05-01 00:03:48  11406446      7       2137.20        131.55   \n",
       "2018-05-01 00:04:48  11406488     13       2948.70         86.42   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-05-01 00:00:48        992.46         350.0  \n",
       "2018-05-01 00:01:48        987.47         368.0  \n",
       "2018-05-01 00:02:48        236.73         182.0  \n",
       "2018-05-01 00:03:48        920.52         305.0  \n",
       "2018-05-01 00:04:48        491.31         226.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#切片2018-05-01~2018-05-11这11天的数据\n",
    "df3 = df2['2018-05-01 00:00:00':'2018-05-12 00:00:00']\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:55:01</th>\n",
       "      <td>12142806</td>\n",
       "      <td>7</td>\n",
       "      <td>1254.99</td>\n",
       "      <td>114.25</td>\n",
       "      <td>284.78</td>\n",
       "      <td>179.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:56:01</th>\n",
       "      <td>12142888</td>\n",
       "      <td>9</td>\n",
       "      <td>1732.75</td>\n",
       "      <td>94.29</td>\n",
       "      <td>354.07</td>\n",
       "      <td>192.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:57:01</th>\n",
       "      <td>12142941</td>\n",
       "      <td>6</td>\n",
       "      <td>1068.78</td>\n",
       "      <td>92.43</td>\n",
       "      <td>304.17</td>\n",
       "      <td>178.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:58:01</th>\n",
       "      <td>12143024</td>\n",
       "      <td>7</td>\n",
       "      <td>2267.69</td>\n",
       "      <td>123.00</td>\n",
       "      <td>819.91</td>\n",
       "      <td>323.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:59:01</th>\n",
       "      <td>12143074</td>\n",
       "      <td>12</td>\n",
       "      <td>3556.14</td>\n",
       "      <td>85.33</td>\n",
       "      <td>1413.76</td>\n",
       "      <td>296.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                         \n",
       "2018-05-11 23:55:01  12142806      7       1254.99        114.25   \n",
       "2018-05-11 23:56:01  12142888      9       1732.75         94.29   \n",
       "2018-05-11 23:57:01  12142941      6       1068.78         92.43   \n",
       "2018-05-11 23:58:01  12143024      7       2267.69        123.00   \n",
       "2018-05-11 23:59:01  12143074     12       3556.14         85.33   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-05-11 23:55:01        284.78         179.0  \n",
       "2018-05-11 23:56:01        354.07         192.0  \n",
       "2018-05-11 23:57:01        304.17         178.0  \n",
       "2018-05-11 23:58:01        819.91         323.0  \n",
       "2018-05-11 23:59:01       1413.76         296.0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用折线图展示连续11天api调用次数趋势\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d'))    #设置时间标签显示格式\n",
    "plt.title('2018-05-01~2018-05-11 API调用次数')\n",
    "plt.plot(df3.index, df3['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:00:00</th>\n",
       "      <td>1.140654e+07</td>\n",
       "      <td>7.300</td>\n",
       "      <td>2165.84400</td>\n",
       "      <td>114.37100</td>\n",
       "      <td>808.2220</td>\n",
       "      <td>304.700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:10:00</th>\n",
       "      <td>1.140733e+07</td>\n",
       "      <td>4.700</td>\n",
       "      <td>1004.64900</td>\n",
       "      <td>112.27200</td>\n",
       "      <td>384.4610</td>\n",
       "      <td>203.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:20:00</th>\n",
       "      <td>1.140808e+07</td>\n",
       "      <td>5.300</td>\n",
       "      <td>1206.68900</td>\n",
       "      <td>101.88700</td>\n",
       "      <td>457.0350</td>\n",
       "      <td>230.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:30:00</th>\n",
       "      <td>1.140879e+07</td>\n",
       "      <td>3.000</td>\n",
       "      <td>824.88750</td>\n",
       "      <td>344.23625</td>\n",
       "      <td>477.5875</td>\n",
       "      <td>392.250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-01 00:40:00</th>\n",
       "      <td>1.140947e+07</td>\n",
       "      <td>1.625</td>\n",
       "      <td>368.44625</td>\n",
       "      <td>133.10875</td>\n",
       "      <td>261.8025</td>\n",
       "      <td>190.375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                             \n",
       "2018-05-01 00:00:00  1.140654e+07  7.300    2165.84400     114.37100   \n",
       "2018-05-01 00:10:00  1.140733e+07  4.700    1004.64900     112.27200   \n",
       "2018-05-01 00:20:00  1.140808e+07  5.300    1206.68900     101.88700   \n",
       "2018-05-01 00:30:00  1.140879e+07  3.000     824.88750     344.23625   \n",
       "2018-05-01 00:40:00  1.140947e+07  1.625     368.44625     133.10875   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-05-01 00:00:00      808.2220       304.700  \n",
       "2018-05-01 00:10:00      384.4610       203.400  \n",
       "2018-05-01 00:20:00      457.0350       230.100  \n",
       "2018-05-01 00:30:00      477.5875       392.250  \n",
       "2018-05-01 00:40:00      261.8025       190.375  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.resample('10T').mean()    #每10分钟取平均值\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:10:00</th>\n",
       "      <td>12139708.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>3626.141</td>\n",
       "      <td>88.452</td>\n",
       "      <td>1040.697</td>\n",
       "      <td>304.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:20:00</th>\n",
       "      <td>12140515.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2071.370</td>\n",
       "      <td>93.607</td>\n",
       "      <td>566.435</td>\n",
       "      <td>219.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:30:00</th>\n",
       "      <td>12141278.8</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1729.872</td>\n",
       "      <td>91.284</td>\n",
       "      <td>527.621</td>\n",
       "      <td>213.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:40:00</th>\n",
       "      <td>12142033.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2375.650</td>\n",
       "      <td>100.721</td>\n",
       "      <td>705.513</td>\n",
       "      <td>258.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-05-11 23:50:00</th>\n",
       "      <td>12142762.0</td>\n",
       "      <td>8.1</td>\n",
       "      <td>1731.094</td>\n",
       "      <td>94.828</td>\n",
       "      <td>511.981</td>\n",
       "      <td>209.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             id  count  res_time_sum  res_time_min  \\\n",
       "created_at                                                           \n",
       "2018-05-11 23:10:00  12139708.5   12.0      3626.141        88.452   \n",
       "2018-05-11 23:20:00  12140515.2    9.2      2071.370        93.607   \n",
       "2018-05-11 23:30:00  12141278.8    8.4      1729.872        91.284   \n",
       "2018-05-11 23:40:00  12142033.3    9.0      2375.650       100.721   \n",
       "2018-05-11 23:50:00  12142762.0    8.1      1731.094        94.828   \n",
       "\n",
       "                     res_time_max  res_time_avg  \n",
       "created_at                                       \n",
       "2018-05-11 23:10:00      1040.697         304.1  \n",
       "2018-05-11 23:20:00       566.435         219.3  \n",
       "2018-05-11 23:30:00       527.621         213.1  \n",
       "2018-05-11 23:40:00       705.513         258.0  \n",
       "2018-05-11 23:50:00       511.981         209.4  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#用折线图展示连续11天api调用次数趋势\n",
    "fig = plt.figure(figsize=(15,5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "ax.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d'))    #设置时间标签显示格式\n",
    "plt.title('2018-05-01~2018-05-11 API调用次数')\n",
    "plt.plot(df4.index, df4['count'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. 分析周末访问量是否有增加。可以发现，周末的下午和晚上，比非周末访问量多一些。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结论：通过统计分析API调用次数，周末的下午14~16点，晚上19~22点这两个时间段(业务高峰期），比非周末访问量要多，其中下午14~16点这个时间段明显增加，晚上19~22点这个时间段略有增加，其他时间段基本持平。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>weekday</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:07</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:07  162542      8       1057.31         88.75        177.72   \n",
       "2017-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2017-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2017-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2017-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg  weekday  \n",
       "created_at                                  \n",
       "2017-11-01 00:00:07         132.0        2  \n",
       "2017-11-01 00:01:07         149.0        2  \n",
       "2017-11-01 00:02:07         169.0        2  \n",
       "2017-11-01 00:03:07         145.0        2  \n",
       "2017-11-01 00:04:07         189.0        2  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#增加weekday一列，代表是周几\n",
    "df2['weekday'] = df2.index.weekday\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:07</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:07</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:02:07</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         id  count  res_time_sum  res_time_min  res_time_max  \\\n",
       "created_at                                                                     \n",
       "2017-11-01 00:00:07  162542      8       1057.31         88.75        177.72   \n",
       "2017-11-01 00:01:07  162644      5        749.12        103.79        240.38   \n",
       "2017-11-01 00:02:07  162742      5        845.84        136.31        225.73   \n",
       "2017-11-01 00:03:07  162808      9       1305.52         90.12        196.61   \n",
       "2017-11-01 00:04:07  162943      3        568.89        138.45        232.02   \n",
       "\n",
       "                     res_time_avg  weekday  weekend  \n",
       "created_at                                           \n",
       "2017-11-01 00:00:07         132.0        2    False  \n",
       "2017-11-01 00:01:07         149.0        2    False  \n",
       "2017-11-01 00:02:07         169.0        2    False  \n",
       "2017-11-01 00:03:07         145.0        2    False  \n",
       "2017-11-01 00:04:07         189.0        2    False  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#增加一列weekend，代表是否是周末\n",
    "df2['weekend'] = df2['weekday'].isin({5,6})\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "weekend\n",
       "False    6.929331\n",
       "True     7.787945\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#按是否是周末分组统计API调用次数平均值\n",
    "df2.groupby('weekend')['count'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按是否周末、小时分组统计API调用次数平均值\n",
    "df2.groupby(['weekend', df2.index.hour])['count'].mean().plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按是否周末、小时分组统计API调用次数平均值，并用折线图展示对比结果\n",
    "df2.groupby(['weekend', df2.index.hour])['count'].mean().unstack(level=0).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
